Essays on Financial Markets and Macroeconomic Performance in Less Developed Economies—Evidence from Egypt

by Ahmed Mohamed Tawfick Rostom

B.A. in Economics, May 1998, Cairo University, Arab Republic of Egypt M.Sc. in Economics, June 2006, Cairo University, Arab Republic of Egypt M.Sc. in Economics and Social Policy Analysis, September 2007, University of York, United Kingdom

A Dissertation submitted to

The Faculty of The Columbian College of Arts and Sciences of The George Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

May 15, 2016

Dissertation directed by

Frederick L. Joutz Professor of Economics

Joseph Pelzman Professor of Economics and International Affairs

The Columbian College of Arts and Sciences of The George Washington University certifies that Ahmed Mohamed Tawfick Rostom has passed the Final Examination for the degree of Doctor of Philosophy as of December 30, 2015. This is the final and approved form of the dissertation.

Essays on Financial Markets and Macroeconomic Performance in Less Developed Economies—Evidence from Egypt

Ahmed Mohamed Tawfick Rostom

Dissertation Research Committee:

Frederick L. Joutz, Professor of Economics, Dissertation Co-Director

Joseph Pelzman, Professor of Economics, International Affairs, Dissertation Co-Director

Hossein G. Askari, Iran Professor of International Business and International Affairs, Committee Member

iii Dedication

To my dear parents (Mrs. Azza Rostom and Mr. Mohamed Rostom), for instilling in me the desire to learn and sacrifice to reach the skies… your affection, love, and day and night prayers made this dream come true.

To my gracious grandmother (Thouraya) R.I.P. for being my First teacher.

To my beloved wife (Amira), her support, encouragement and unconditional love have sustained me throughout the way.

To my adorable kids (Ibrahim and Nazly), your beautiful smiles reassured me and continued to endow me with hope.

To my caring sister (Yasmine) for believing in me.

To my supportive parents in law (Dr. Maged Aly and Mrs. Fatma Abdelbaset) for their warm passion.

This document is a testament to the family’s patience, encouragement and faith throughout the years.

All Praise be to God for the kind blessing.

iv Acknowledgements

I’m very thankful to my dissertation directors and committee members for all their help and encouragement. Additionally, I would like to thank external readers,

Professor Dr. Roberto Samaniego and Professor Dr. Alaa El-Shazly, for tirelessly reading my dissertation and providing valuable comments for improvement.

I’m also grateful to His Excellency Professor Dr. Hazem El Beblawi, Professor Dr.

Laila El-Khawaga, and Professor Dr. Mahmoud Mohieldin for their invaluable guidance.

I would also like to thank Dr. Khaled Sakr, Dr. Moataz El-Said, Dr. Subramanian

Sriram, Dr. Mai Farid, and Mr. Amgad Hegazy from the International Monetary

Fund; Dr. Fakhri Hasanov from King Abdullah Petroleum Studies and Research

Institute; Dr. Subika Farazi and Mrs. Lamia Donia from the World Bank, Mr. Yasser

Tawfik from the International Finance Corporation and Mr. Moataz Yeken and Ms.

Nesma Gad from the Egyptian Ministry of International Co-operation.

I also remember with grace Professor Dr. Manal Metwali for her encouragement and discussions during early days of this research effort. God rest her soul in peace.

I’m very grateful to my classmates Dr. Ferhat Bilgin, Dr. Murat Issabayev, and Mr.

Joseph R. McCormack, for all the deep discussions and sharing of thoughts.

To all brave Egyptians sacrificing their lives to defend the country and to realize the dream of a better Egypt.

v Abstract of Dissertation

Essays on Financial Markets and Macroeconomic Performance in Less Developed Economies—Evidence from Egypt

The objective of my dissertation research is to contribute to the quantitative-based research and policy discussion concerning the links between macroeconomics, monetary policy, and the financial sector. My dissertation is motivated by the fact that financial intermediation plays a critical role in the economic development of low income economies. Assessment of financial sector performance is instrumental towards understanding the dynamics of economic growth. My dissertation’s contribution to the empirical literature about less developed economies stems from the application of rigorous and robust econometric techniques to high quality data.

This paves the way forward to better assess the effectiveness of monetary policy and efficiency of financial intermediation in these economies. My analysis, which focuses on Egypt, takes into consideration various political, economic, and social transformations that took place during the past five decades and which have substantially affected the macroeconomic performance of the country.

My dissertation follows the primary model for dissertations and is divided into three different essays. In the first chapter, I examine the Demand for Money in Egypt. In the second chapter, I investigate the presence of a Broad Credit Channel for Monetary

Transmission. In the third essay, I present an Aggregate Model for Savings Behavior and its Determinants in Egypt. The objective and proposed scope of my research is detailed below.

vi In the first chapter, I study the money demand critical for defining monetary policy options. In the context of developed economies with fully functioning financial markets, money demand is influenced by transaction demand, speculation motive, and opportunity costs. This doesn’t hold for less developed economies where market imperfections and frictions can play a key role in money demand dynamics. These factors weren’t adequately covered in the empirical literature. In this chapter, I attempt to cover these issues and contribute to the empirical literature, with particular emphasis on Egypt. In the chapter, I provide a mixed strategy for estimating the money demand function that incorporates shifting from a system of equations formulated within the Vector Auto Regression (VAR) approach that maintains the information relating to the feedback between variables, followed by the Vector

Equilibrium Correction Model (VECM) in order to investigate the nature of short term and long term interactions. My conclusion is that real money demand in Egypt is stable and can be considered confidently by monetary authorities as an important tool to adjust for long run growth in the real economy.

In the second chapter, I focus on the credit channel for monetary policy in Egypt. Past research has shown that healthy financial intermediation should be able to support monetary policy and has the potential to mitigate transmitting economic shocks to the real sector (Bernanke, 2007; and Stein, 1998). However, most past research has focused on developed economies with little research devoted to low income countries.

Research was devoted to less developed economies despite their relatively higher vulnerability to volatility, political instability, and difficulty obtaining reliable data

(Fernandez, 2003). The objective of this dissertation is to fill this gap in the literature

vii and, in particular, provide answers to three main questions regarding the conduct of monetary policy in Egypt: (i) Does bank credit operate as a transmission channel for monetary policy and, if not, is there another channel that transmits monetary policy in

Egypt? (ii) Is the credit channel important to guide monetary authorities and the financial economics profession? (iii) And finally, how can Structural Vector Auto

Regression (SVAR) be adopted to model monetary policy strategy for Egypt? My findings confirm the existence of a statistically significant credit channel for monetary policy when the monetary base is seen as an operating target. The key outcome is that the credit channel, if properly considered, can help authorities propagate monetary policy shocks to the real economy.

In the third chapter, I study the behavior of savings in Egypt. Private sector savings play a pivotal role in financing development and sustaining growth. Understanding the dynamics of determinants of savings is crucial to inform economic policy and devise reform programs. The Egyptian government adopted an economic reform program in the 1990s, the main pillar of which was to support financial development and achieve economic growth. This followed a full liberalization of interest rates, allowing markets to determine the opportunity cost of holding money. The purpose of this dissertation is to investigate for the first time private savings behavior in Egypt using quarterly data covering the period 1991–2010 and adopting a VECM. The key finding is that in the long run, Egypt’s private savings follow the Life Cycle Model proposed by Modigliani

1954. Controlling for population growth, empirical results indicate that in the long run real interest rates and financial development are key determinants for real private savings. However, in the short run, economic uncertainty proxied by inflation and

viii devaluation of the exchange rate are key determinants of private savings decisions.

Robust macroeconomic and monetary policies are prerequisites to maximize private savings and financial growth in Egypt.

ix Table of Contents

Dedication37T 37T iv

Acknowledgements37T 37T v

Abstract37T of Dissertation 37T vi

Chapter37T I: Estimating Money Demand for Egypt 37T 1

Abstract37T 37T 1

1.37T Introduction 37T 2

2.37T Literature Review 37T 6

i.37T Recent Developments in Global Literature on Money Demand 37T 8

ii.37T Literature on Money Demand in Egypt 37T 12

iii37T Potential Areas for Improving Empirical Investigation of Money Demand

in Egypt 37T 18

3.37T Theoretical Motivation 37T 19

4.37T Hypothesis to be Tested 37T 23

5.37T The Variable Selection and Description of Data 37T 25

i.37T Variable Selection 37T 25

ii.37T Descriptive Analysis of Key Variables 37T 28

6.37T Econometric Methodology and Modeling Strategy 37T 32

7.37T Empirical Results 37T 36

i.37T Cointegration Under Closed Economy Formulation 37T 38

ii.37T Cointegration Under Open Economy Formulation 37T 57

8.37T Conclusion 37T 68

Annex37T I: Summary of Findings of the Research on Estimating Money Demand for

Egypt 37T 70

Annex37T II: Summary Tables and Integration Tests for Money Demand Model 37T 76

Annex37T III: Diagnostic Tables for Closed Economy Formulation 37T 80

Annex37T IV: Graphical Representation and Diagnostics for Closed Economy

Formulation 37T 91

Annex37T V: Diagnostic Tables for Open Economy Formulation 37T 102

Annex37T VI: Graphical Representation and Diagnostics for Open Economy

Formulation 37T 112

References37T 37T 122

Chapter37T II: Investigating the Existence of a Credit Channel in Egypt 37T 131

x Abstract37T 37T 131

1.37T Introduction 37T 132

i.37T Interest rate channel 37T 137

ii.37T Exchange rate channel 37T 138

iii.37T Asset prices channel 37T 139

iv.37T Credit channel 37T 140

2.37T Theoretical Motivation 37T 142

3.37T Literature Review on the Credit Channel of Monetary Policy 37T 145

i.37T Recent Developments in Literature on the Credit Channel of Monetary

Policy in Developed Economies: 37T 145

ii.37T A Critical Review of the Literature on the Credit Channel of Monetary

Policy in Egypt 37T 151

iii37T Contribution to the Empirical Literature: 37T 158

a.37T Encompassing weaknesses in earlier research 37T 159

b.37T Ensure better data quality 37T 160

4.37T Hypothesis to Be Tested and Modeling Strategy 37T 161

5.37T Data and Variables Selection 37T 172

6.37T Empirical Results 37T 174

i.37T Monetary Base as a Measure of Monetary Policy Stance (Model B) 37T 176

ii.37T Lending Rate as a Measure of the Monetary Policy Stance (Model R) 37T 180

7.37T Granger Causality 37T 184

i.37T Monetary Base as a Measure of Monetary Policy Stance (Model B) 37T 184

ii.37T Lending Rate as a Measure of Monetary Policy Stance (Model R) 37T 186

8.37T Structural Impulse Response Functions 37T 187

i.37T Monetary Base as a Measure of Monetary Policy Stance (Model B) 37T 188

ii.37T Lending Rate as a Measure of Monetary Policy Stance (Model R) 37T 190

9.37T Variance Decomposition 37T 192

i.37T Monetary Base as a Measure of Monetary Policy Stance (Model B): 37T 192

ii.37T Model R: Lending Rate as a Measure of Monetary Policy Stance 37T 194

10.37T Conclusion 37T 196

References37T 37T 199

Annex37T VII: Summary of Findings of the Empirical Research on the Credit Channel

as a Monetary Transmission Mechanism in Egypt 37T 203

xi Annex37T VIII: Summary Tables and Integration Tests 37T 205

Annex37T IX: Diagnostic Tables for Recursive VAR Identification Strategy (Cholesky

Decomposition): Monetary Base Model (Model (B)) 37T 209

Annex37T X: Graphical Representation and Diagnostics for Recursive VAR Identification Strategy (Cholesky Decomposition): Monetary Base Model (Model

(B)) 37T 213

Annex37T XI: Structural VAR Estimates: Non - Recursive VAR Identification Strategy

(Theory Based Restrictions): Monetary Base Model (Model (B)) 37T 218

Annex37T XII: Granger Causality, Impulse Response and Variance Decomposition

(tables): Monetary Base Model (Model (B)) 37T 220

Annex37T XIII: Granger Causality, Impulse Response and Variance Decomposition

(graphs): Monetary Base Model (Model (B)) 37T 226

Annex37T XIV: Diagnostic Tables for Recursive VAR Identification Strategy (Cholesky

Decomposition): Lending Rate Model (Model (R)) 37T 229

Annex37T XV: Graphical Representation and Diagnostics for Recursive VAR

Identification Strategy (Cholesky Decomposition): Lending Rate Model (Model (R)) 37T 233

Annex37T XVI: Structural VAR Estimates: Non - Recursive SVAR Identification

Strategy (Theory Based Restrictions): Lending Rate Model (Model (R)) 37T 238

Annex37T XVII: Granger Causality, Impulse Response and Variance Decomposition

(tables): Lending Rate Model (Model (R)) 37T 240

Annex37T XVIII: Granger Causality, Impulse Response and Variance Decomposition

(graphs): Lending Rate Model (Model (R)) 37T 246

Chapter37T III: Savings Behavior in Egypt 37T 249

Abstract37T 37T 249

1.37T Introduction 37T 250

2.37T Recent Literature Review on Modeling Saving Behavior 37T 252

i.37T Recent Developments in Literature on Saving Behavior in Emerging and

Developing Countries 37T 252

ii.37T Literature on Saving in Egypt 37T 257

iii.37T Potential Areas for Improving Empirical Investigation of Savings Behavior

in Egypt 37T 262

3.37T Theoretical Motivation 37T 263

4.37T Hypothesis to be Tested, Variables Selection, and Data Issues 37T 266

5.37T Economic Methodology and Modeling Strategy 37T 274

xii 6.37T Empirical Results 37T 278

i.37T Testing for Integration 37T 278

ii.37T Cointegration Analysis 37T 280

iii.37T Selecting the Lag Length 37T 282

iv.37T Co-integration Analysis 37T 284

v.37T Long-run Modeling 37T 285

vi.37T Real Interest Rate 37T 288

vii.37T Financial Development 37T 289

viii.37T Short-run Modeling 37T 290

ix.37T Reduction of the General Model and Parsimonious Model 37T 293

x.37T Key Economic Findings of the Short Run Parsimonious Model 37T 295

7.37T Conclusion 37T 297

References37T 37T 299

Annex37T XIX: Key Findings of Existing Empirical Literature on Savings Behavior in

Egypt 37T 303

Annex37T XX: Summary Tables and Integration Tests for Savings Behavior Model 37T 310

Annex37T XXI: Diagnostic Tables for Savings Behavior Model 37T 314

Annex37T XXII: Graphical Representation and Diagnostics for Savings Behavior Model 37T 321

xiii Chapter I: Estimating Money Demand for Egypt

Abstract

Money demand is critical for defining monetary policy options and is not driven necessarily by developed country standards of transaction demand, speculation motive, and opportunity costs grounded by fully functioning financial markets. However, market imperfections in less developed economies can also play a critical role in the dynamics of demand for money. This is not covered adequately in current empirical literature for less developed economies, particularly in Egypt. In this chapter, I investigate the empirical relationship among real money demand, real income, interest rates, and real consumption in Egypt. I provide a mixed strategy for estimating the money demand function that incorporates shifting from a system of Equations Vector Auto Regression (VAR) approach that maintains the information relating to the feedback between variables, followed by Vector Equilibrium Correction Model (VECM) data in order to investigate the nature of short term and long term interactions. At the end of the chapter, I conclude that real money demand in Egypt is stable and can be considered confidently by monetary authorities to adjust for long term growth in the real economy.

JEL codes: C32, C52, E41, E65.

Keywords: Time-Series Models, Model Evaluation, Validation, Selection, Money

Demand, Economic Reform Policies

1 1. Introduction

In essence, money is a financial asset that facilitates the interchange of goods and services. People demand money for several reasons: to purchase goods and services now

(transaction motive) or in the future (precautionary motive); or as economic agents to conserve value over time (reserve of value motive). Moreover, demand for money is also considered an important function of stabilization and structural adjustment policies where such policies depend on the ability to adjust money supply to its demand to prevent monetary disturbances from affecting real output. Therefore, understanding the dynamics of money demand is essential in macroeconomic policy and management.

In addition, quantitative analysis plays a significant role in specifying money demand equations in order to assist the policy making process, especially in monetary policy.

Robust modeling of money demand functions helps to influence monetary aggregates on output, interest rate, and consumption.

The literature on money demand focuses on developed and stable economies. There was no, or very little if any, attention paid to less developed—transitioning—open economies

1 in the published literature, particularly during the past five years P0F .P These economies consist of a group of countries that have undergone social, economic, and political

2 transition during the past five years of what is called the “Arab Spring” P1F .P

1This statement is based on thorough investigation that I did to the published literature during the past five years in key, high impact factor academic journals, 2 This term is usually used to reference countries that witnessed political regime changes in the Middle East during the past five years including Egypt, Tunisia, Libya, Yemen and Syria.

2 Some of these economies still struggle with political turbulence, such as Syria and Libya, while others–including Egypt and Tunisia,—are struggling for economic recovery. A deeper understanding of how the recent political and social turmoil affected the monetary and real economy, as well as liquidity preference and portfolio composition in these countries, will definitely be of interest to the economics profession.

These economies were subject to several exogenous shocks that reflected on the monetary dynamics, price levels, and therefore the pattern of economic growth.

Moreover, the nature of imperfect financial markets and huge uncertainties, as well as fiscal dominance inherent in these economies, impair the ability of monetary authorities to maintain price stability and challenge their choice of relevant operational and intermediate targets (Alvarez et. al. 2009). Despite the fact that many central banks in these economies explicitly announced targeting inflation and abandoning exchange rates as nominal anchors, stable money growth remains a cornerstone for these central banks to control inflation (Jahan 2012).

With respect to Egypt, money demand represents the main tool used by Egyptian authorities to conduct monetary policy. Other monetary policy channels are still in the development phase. Historically, monetary authorities in Egypt relied on a money supply tool through contractionary monetary policy to control the inflation rate and boost real economic growth rate and per-capita income (Kheir-El-Din 2008).

In light of this, this dissertation investigates an empirical model of money demand in

Egypt over the past six decades and ensures that economic policy reforms are in place.

Egypt’s monetary and financial sectors qualify as an ideal laboratory that can well

3 represent the space of less developed economies. The objective of this dissertation is to answer a set of key research questions that would help the economics profession better understand the monetary and financial economics of less developed—transitioning— economies with application to Egypt. I will examine factors that influence the demand for money, and will answer questions relating to the stability of money demand, and the attachment of unitary income elasticity in alignment with Friedman’s Quantity Theory of

Money (QTM). Also, I will assess the nature of the relationship between monetary aggregates and price levels.

The novelty and authenticity of this chapter of the dissertation will contribute in several respects to the empirical literature on money demand.

First, it bridges the gap in the current literature in the understanding of the short and long run dynamics of monetary aggregates, interest rates, exchange rates, and output in less developed—transitioning—open economies.

Second, it provides in-depth quantitative analysis and employs robust econometric modeling techniques using high quality data.

Third, the data set that covers the period under study, i.e., the past 60 years, is rich with structural events and exogenous shocks that would contribute to a better understanding of monetary dynamics. This includes three revolutions that occurred in 1952, 2011, and

2013 and five wars (Suez Crisis, 1956, the Arab-Israeli War, October, 1973, a major terrorist attack on tourists in 1986, the First Gulf War, 1991, and the Second Gulf War,

2003). I will track the developments in Egypt’s economy closely, from a centralized command during the period 1950–1975, to full economic liberalization in 1976, then

4 domestic economic and financial crises in the late 1980s, followed by a stabilization program lead by the International Monetary Fund (IMF) during the 1990s and early

2000s. On the global level, this entire time span well covers the implications of the global oil crises in the 1970s, the Asian crises of the 1990s, and the global food and financial crises in the early 2000s, the global financial crises in 2008, and finally the Euro zone crises in 2011. These events have reflected on the portfolio composition and preference

3 of individuals, as well as the aggregate demand for liquid assets. P2F P Moreover, the shift from strict capital controls in the 1970s to a fully open economy since the 1980s, followed by a shift from a fixed exchange rate in 2003 towards a crawling peg and, finally, abandoning the exchange rate as a nominal anchor and adopting a—non operative—inflation targeting framework for monetary policy in 2004. All these events have imposed many queries on the response of monetary aggregates during the past six decades on the nature of long term relation between monetary aggregates and real economic activity in a robust and coherent manner.

The goal is to contribute in a comprehensive approach to a better understanding of monetary dynamics within less developed economies. The variety of shocks and transformations support the argument that Egypt is a feasible candidate for testing the stability of money demand in less developed open economies that are undergoing political and social transitions.

3For a detailed assessment on how the crisis affected the Egyptian economy please see the World Bank’s report “Egypt and The Global Economic Crisis: A Preliminary Assessment of Macroeconomic Impact and Response”, accessible at http://siteresources.worldbank.org/INTMNAREGTOPPOVRED/Resources/MacroPolicyNoteG rayCoverVolume1June16.pdf

5 This chapter is organized as follows: section 2 reviews the recent relevant literature on money demand; section 3 describes the theoretical motivation; section 4 summarizes the hypothesis to be tested, and section 5 explains the selection of variables and provides a brief description of the data. Section 6 describes the econometric methodology and modeling strategy; and section 7 the analysis estimation and testing. Section 8 provides a conclusion and a set of policy recommendations.

2. Literature Review

Understanding the relationship between money, output, and interest rate is key for managing macroeconomics and determining priorities of monetary policy. The European

Central Bank continues to assign a prominent role to money in its monetary policy

4 strategy P3F .P Moreover, while the United States Federal Reserve Board relied less on money when conducting monetary policy, money is still the vehicle by which the Board exerts direct control over bank reserves. This enables it to influence the federal funds rate,

5 thereby broadening economic conditions P4F .P As a result, money demand has received great attention in the literature.

Golfeld and Sichel (1990); Fase (1993); Sriram (2001); and Knell and Stix (2006) provided a comprehensive review of 198 studies contributing to the literature on money demand since 1972. These surveys outlined the rationale for testing the stability of the

4 The ECB calls its two-pillar strategy; one pillar is “economic analysis," which assesses the short-to- medium-term determinants of price developments." According to the ECB, this analysis takes account of the fact that price developments over those horizons are influenced largely by the interplay of supply and demand in the goods, services and factor markets." But in addition, a second pillar, “monetary analysis", assesses the medium-to-long-term outlook for inflation, exploiting the long-run link between money and prices." The two alternative frameworks for assessing risks to price stability are intended to provide cross-checks" for one another (ECB, 2004, p.55). 5 See Richmond Fed Economic Brief 2013, accessible at https://www.richmondfed.org/publications/research/economic_brief/2013/pdf/eb_13-05.pdf

6 money demand function, particularly in the long run. This is a necessary condition for sustainable and balanced economic growth in the long run.

6 Poole (1970) P5F P emphasized the need to understand money demand for the design and implementation of optimal monetary policy. In his view, the monetary authority should compare the volatility of money demand to aggregate expenditure variability, the sole source of instability in aggregate demand with an interest-rate instrument.

Friedman (1975) showed that understanding money demand is highly relevant, particularly in an environment where a central bank chooses to use a monetary aggregate as an intermediate target. Its usefulness appears to be measured in terms of enhanced stability of output, increasing with lower money demand elasticity and with greater money demand stability.

On the other hand, Meltzer (2002) contributed to a more rigorous understanding of changes in the real monetary base and its influence on real output in the United States

(US) and the United Kingdom (UK), and demonstrated that it was independent of effects arising through the channel of short-term interest rates.

Nevertheless, the role of money demand in the formulation of monetary policy was recently debated in the monetary literature. There were arguments that money demand has a minor role under an interest-rate-based (Taylor-rule type) monetary policy

(McCallum, 2003). However, Duca and VanHoose (2004) carefully voted for the continued relevance of the money demand in conducting monetary policy. They argued that monetary policy does not work only through the interest rate channel, and that money

6 He is a strong supporter to the role of money demand in conducting monetary policy.

7 demand can provide useful information about liquidity and portfolio allocations. This clarified why monetary authorities in advanced economies (US, UK, Germany, Australia, and others) give special attention to modeling the behavior of the demand for money to ensure effectiveness of monetary policy. i. Recent Developments in Global Literature on Money Demand

The empirical modeling of money demand witnessed substantial progress during the past five years to take into account many factors that influence desired levels of money holdings within a panel of countries as well as on an individual country level. What follows is a brief review of key contributions to the literature subsequent to the most recent comprehensive literature review by Knell and Stix (2006).

Panel studies focused on addressing issues of uncertainty and the impact of such uncertainty on money holdings, and also looked at how data aggregation impairs the econometric findings due to heterogeneity of agents on the micro level. The impact of uncertainty on money demand in six Central and Eastern European emerging economies and four other emerging economies was considered by Oskooee, Kutan and Xi (2013).

Results confirmed that the uncertainty’s impact is transitory in most countries and money demand was correctly specified and stable in most countries.

Bahmani and Kutan (2010) tested the stability of money demand for Armenia, Bulgaria, the Czech Republic, Hungary, Poland, Russia, and Slovakia. They used the bounds testing approach to co-integration that shows stable money demand in these countries.

To address issues of data aggregation, Hsiao, Shen and Fujiki (2005) used Japanese aggregate and disaggregate money demand data to show that conflicting inferences can

8 arise. Aggregate data appear to support the contention that there was no stable money demand function, while disaggregate data showed that there was a stable money demand function. The diametrically opposite results are attributed to the neglected heterogeneity among micro units.

During the past five years, new modeling techniques have evolved for country level research related to money demand. Focus was directed to investigating the presence of a non-linear relationship between monetary aggregates and interest rate, as well as the possibility of considering time-varying behavior of coefficients.

Lee and Chang (2013) employed second-generation Random Coefficient (RC) modeling to investigate the time-varying behavior and the predictability of the money demand function in Taiwan over the period 1982Q1 to 2006Q4. Empirical results indicated that the values of elasticity in the RC estimation are significantly different from those in other studies, because of the use of coefficient drivers that can make an association with real events occurring in Taiwan, such as the financial liberalization after 1989 and the Asian financial crisis of 1997–1998.

Bae and De Jong (2007) combined the logarithmic specification, which models liquidity better than a linear model, with the assumption that the interest rate itself is an integrated process. Results for the US produced larger coefficient estimates than previous research in addition to superior out-of-sample prediction.

Recent research also challenged the conventional wisdom of instability of demand for narrow money in many advanced economies across the globe (mainly the US, Australia, and Italy). These studies focused on the relevance of the opportunity cost for money

9 particularly in the short run in addition to the redeployment of classical modeling technologies in the short and long runs using partial adjustment models and autoregressive distributed lag models. Moreover, the most recent studies also deployed

7 Pesaran et al. (2001)’s bound testing approach for co-integration. P6F P What follows is a detailed account of these contributions, upon which I have built my dissertation proposition, to model and test the stability of demand for narrow money in Egypt.

In his paper, Ball (2012) challenged the conventional wisdom about the instability of the short-run money demand by examining quarterly data for the US from 1959 through

1993. Following the work of Hoffman and Rasche (1991) and Stock and Watson (1993),

Ball interpreted long-run money demand as a co-integrating relationship among real narrow money balances M1 interest rates and output. To explain short-run deviations from the long-run relationship, Ball used Goldfeld’s partial-adjustment model. The key innovation of his paper is the choice of the interest rate in the money demand function.

Instead of using a short-term market rate, such as the treasury bill rate or the commercial

8 paper rate, Ball used the average return on ‘‘near-monies’’ P7F .P His findings showed that the long-run money demand is stable regardless of whether the interest rate is measured by the return on near monies or a money-market rate (the T-bill rate). However, the deviations of money holdings from their long-run levels are smaller with the return on near monies. In addition, Goldfeld’s partial-adjustment model yielded reasonable parameter estimates using the return on near monies, and it is not rejected in favor of a

7The autoregressive distributed lag (ARDL) approach of Pesaran et al. (2001) tests for existence of a relationship between series irrespective of whether they are I(0), I(1), or mutually co- integrated compared to the method developed by Johansen (1988) and Johansen and Juselius (1990) that require all the variables in the system be of equal order of integration. 8 Near- Monies is defined as savings accounts and money market mutual funds that are close substitutes for M1.

10 less structured error-correction model. Most importantly, he found that the deviations of money holdings from the predictions of the model are small. Thus, there is little evidence of shifts in the money demand function, even in the short run.

Hossain (2012) also challenged the conventional wisdom of the instability of money demand function in developed countries since the mid-1980s. He investigated the presence of an economically meaningful, stable narrow money demand relationship using annual data for Australia for the period 1970–2009. He employed an Auto-regressive

Distributed-lag (ARDL) co-integration approach. The results suggested the presence of a long-run equilibrium relationship between real narrow money balances, real income, a representative domestic interest rate (e.g., the yield on Australian government short-term bonds) and the nominal effective exchange rate of the Australian dollar. The statistical tests suggested no significant instability in the narrow money demand relationship despite financial deregulation and innovation in Australia since the early 1980s. In contrast, the paper reported statistical results which suggest no meaningful, stable broad money demand relationship in Australia over the sample period. These results implied that while financial deregulation has made the broader monetary aggregates more volatile, given the increasing availability of financial assets with competitive returns, there have been only steady changes in transaction technology in Australia since the early 1980s; therefore, the demand for narrow money has remained stable. An implication is that money remains important for Australia in its use of monetary policy to target price stability. Thus, narrow monetary aggregate can be used to measure excess liquidity for forecasting or predicting its impact on inflation, asset prices, and the output gap.

11 Capasso and Napolitano (2012) investigated the consistency of the stability of money demand in Italy, before and after the European Monetary Union (EMU) and tried to determine the effect of a change in the currency regime on the monetary aggregates. They estimated the money demand function in Italy using quarterly data from 1977 to 2007, using the bounds testing co- integration procedure proposed by Pesaran et al. (2001). In order to compute the short- and long-run elasticities of the demand for money, they implemented Cumulative Sum (CUSUM) and Cumulative Sum of Squares (CUSUMSQ) stability tests proposed by Brown et al. (1975). The analysis is performed on broader money M2 and M3 also. Their findings revealed significant stability of money demand

9 coefficients P8F P in the short- and long-terms. This result applied to both M2 and M3. In order to further investigate long-term dynamics in the coefficients of money demand,

Pesaran, et al. also employed Kalman filtering (Kalman 1960). They found that the introduction of the Euro has significantly increased the stability of money demand parameters particularly for M3, and the effects occurred before the introduction of the new currency in 1996 when the Lira was strongly linked to the Euro in preparation to the

EMU. ii. Literature on Money Demand in Egypt

With regard to Egypt, ten research papers studied money demand during the past three decades; the most recent was issued four years ago. As part of my research plan, I conducted a comprehensive review for modeling money demand in Egypt. These studies are reviewed in detail in the Appendix to this chapter.

9 Income, interest rate and nominal effective exchange rate.

12 Scobie (1983) is one of the early empirical studies on estimating money demand in

Egypt. It was conducted to examine the monetary impact of subsidy expenditures on domestic inflation, the balance of payment, and the exchange rate. These variables were determined simultaneously within a framework of the monetary approach of the balance of payment. The demand for real money balances was estimated within a system of four structural equations, including domestic inflation, the black market exchange rate, and the balance of payments equation by adopting a two stage least squares estimation using annual data for the period 1947–1981. The study concluded that the elasticity of real money demand was estimated to be 0.38 in the short run and 1.42 in the long run. With respect to inflation, it was found to be -0.86 in the short run and -2.71 in the long run.

El-Erian (1987) adopted Ordinary Least Squares (OLS) framework to identify the absolute and relative determinants of currency substitution that took place during the period 1980–

1986. He used data related to individuals’ expectations regarding foreign balances. A reduced form of the money demand model was estimated using quarterly data of relative holdings of foreign currency deposits and specifications of the associated impact on the portfolio balance decisions of asset holders. Empirical results indicated that agents tend to hold foreign balances for two main reasons: (1) higher financial yields due to expectations of exchange rate induced capital gains; and (2) greater political stability and fewer institutional changes, especially banking regulations. Results also proved that the impact of enhancing these causal factors to influence agents to limit currency substitution will require a time lag to alter the actual holdings of foreign currency balances.

Alami (1999) tested for a long-run equilibrium relationship (co-integration) between the ratio of foreign currency denominated deposits to total liquidity, the expected rate of

13 change of the exchange rate, and an interest rate differential between domestic and foreign currency deposits. He used quarterly data for Egypt covering the period 1981:IV to 1993:IV and tested for co-integration among the different variables utilizing the Engle-

Granger methodology (Engle and Granger 1987). The co-integration results were further assessed by implementing the Johansen methodology (Johansen and Juselius 1990) to confirm the uniqueness of the co-integrating vector. Finally, Alami estimated a dynamic error correction model for the dollarization ratio. The results showed that demand for foreign currency deposits varies with changes in the interest rate differential and not the exchange rate, which implies that the reason behind holding foreign currency denominated deposits is related to portfolio shifts and not to currency substitution, i.e.,

Egyptian residents substitute the currency in an attempt to preserve the value of money rather than using it as a medium of exchange. The empirical evidence in this paper suggested that foreign currency denominated deposits will have no significance when studying money demand as a monetary instrument.

Handy (1999) estimated a standard money demand function to measure the impact of inflation on money demand in Egypt using annual data during the period 1970/71–

1996/97. Nominal monetary aggregate was regressed on the Consumer Price Index (CPI), real gross domestic product index, inflation rate, average interest rate measured in local currency, and an equivalent offshore interest rate using the augmented ARDL procedure.

To check the stability of the money demand function, the monetary aggregate was identified first as currency in circulation, then as domestic liquidity, excluding foreign currency deposits, and finally, as total liquidity. The results indicated that: (1) money demand in Egypt is strongly related to price level, real output, and inflation; (2) in the

14 second half of 1980s, there was a tendency towards dollarization that took the form of foreign currency deposits; and (3) the two forms of money defined as currency in circulation and total liquidity were robust over the period. Thus, targeting money demand should not be regarded when setting monetary measures in Egypt.

El-Sheikh (2002) estimated demand function of M1 using quarterly data covering the period 1960–1973 using the maximum likelihood technique. M1 was regressed on the expected real income, seasonal income variations, a set of relevant yields, and a set of institutional, distributional, and war variables. The following variables were found to be significant in determining M1 demand; saving deposit yields, post-office deposit yields.

The study concluded with four important findings related to the estimation of the money demand function: First, the estimate of the elasticity of M1 with respect to seasonal income was far below unity, while the corresponding estimate with respect to expected income was far higher than unity. The estimate of the elasticity of M1 with respect to long run seasonal income is in line with those estimated by Baumol (1952) and Tobin (1956). El-Sheikh was able to differentiate between holding the currency to preserve the value of the money rather than using it as a medium of exchange. He argued that, during seasonal times in Egypt, currency is mainly used for transaction purposes, but in off peak periods, currency will be held also as an asset. Second, contrary to the case in developed economies, the quarterly adjustment is conducted slowly. Third, the estimated long run elasticity of M1 with respect to the interest rate exceeded the values found by other studies in developed economies. El-

Sheikh suggested that the narrow asset menu available in Egypt might be the reason behind this finding. Finally, assuming there is no change in the structural behavior of the asset

15 holder following the studied period, the Egyptian government should refer to money demand in Egypt when designing monetary policy.

Baliamoune-Lutz and Haughton (2004) tested Friedman’s hypothesis with application to

Egypt. The research question was whether increased variability in the growth of the money supply causes velocity to decline. They used annual data from the period 1960–

99. The monetary aggregates M1 and M2 are decomposed into anticipated and unanticipated components, and the variability of money growth is computed as the standard deviation of five years of monetary growth rates. Applying Johansen and

Juselius’ (1992) co-integration tests provided evidence that in the case of Egypt there is a statistically significant long-run relationship between the variability in money growth and velocity for both M1 and M2. However, while increased variability in the growth of M2 is found to be associated with lower velocity, supporting Friedman’s velocity hypothesis, increased variability in the growth of M1 seems to have no influence on velocity, possibly because the definition of M1 has changed over time. The findings also suggested that anticipated movements in M2 volatility are not neutral, in the sense that they do affect velocity. The main contribution of this research is that in the short run, discretionary monetary policy in Egypt is somewhat circumscribed and constrained.

However, if the Central Bank of Egypt were to make its decisions more transparent and pre-announce its policies, then velocity would be more predictable and monetary policy would be more potent and effective.

In an attempt to investigate whether Egypt should implement an inflation-targeting approach to pursue its monetary policy, the Central Bank of Egypt’s annual report for the year 2010 examined the stability of the money demand function. Using monthly data over

16 the period July 1997–June 2009, this exercise starts by estimating the long-run co- movement vector based on the Johansen method. The model incorporated data on real

GDP, 3-month Treasury bill rate, annual inflation rate, and an impulse dummy to account for the exchange rate regime change in 2003, while 3-months deposit rate, and expected depreciation of bilateral nominal exchange rate were found to be insignificant. The model turned out to be well specified since the wealth effect approached positive unity. Also, it was empirically found that the Treasury bill rate weakly impacted money demand, while inflation rate is highly related to money demand. Then in order to identify the short-run dynamics and structural adjustments, the Error Correction Model was used. The results showed that only the Treasury bill rate and expected inflation rates affect the money demand short-run dynamics, while real GDP was found to be insignificant in the short run. Finally, this paper estimated the Reduced Form to assess the stability of the money demand function. There no evidence was found to signify the instability of the money demand function, neither in the long run nor in the short run, except for the period (2005–

2007) during which two structural shocks occurred: the implementation of a new monetary policy framework, and the avian flu epidemic. It concluded by advising the

Egyptian government not to shift to the inflation-targeting framework.

In the context of purposing an effective monetary targeting regime in Egypt, Awad

(2010) estimated the long run money demand function and tested its stability using quarterly data covering the period 1995–2007. Money demand was estimated as a function of the following variables: real income, lagged inflation, a nominal exchange rate, and the 3-month deposit interest rates. Awad adopted the Johansen co-integration test, and variables were co-integrated of order one. Using the OLS framework, as well as

17 Durbin-Watson and Breusch-Godfrey serial correlation, a 19TLagrange Multiplier 19T (LM) test detected first order serial correlation in the residuals. Accordingly, an autoregressive term was included in the regression equation as an explanatory variable and the model was re- estimated. The main findings confirmed that the money demand function was proven to be unstable via both the Chow breakpoint test and Chow forecast test (Chow 1960) and using both nominal and real exchange rates. Thus, it was concluded that the Central Bank of Egypt (CBE) should alter its monetary policy from monetary targeting regime to inflation targeting regime to achieve price stability. iii Potential Areas for Improving Empirical Investigation of Money Demand in Egypt

The aforementioned literature review shows that earlier research left many gaps that need to be filled; this dissertation will contribute to filling these gaps as follows:

• Longer Time Horizon: Eight studies concluded that money demand in Egypt

was stable during various intervals; many of which missed key events mentioned

in the theoretical motivation section above. This research will encompass previous

studies through analyzing over a longer period to ensure robustness of the analysis

and soundness of tests for co-integration and quality of inference in both the short

and the long runs.

• Financial Market Imperfection: This dissertation will incorporate a proxy for

market imperfection in the money demand function. This was overlooked in

earlier money demand studies in Egypt despite the existence of a significant

18 10 parallel exchange rate market. P9F P The premium of the parallel exchange rate over

the official market rate is considered as a proxy for market imperfection and

currency substitution. This is relevant to the less developed economies that suffer

distorted financial markets (Osokee et. all 2011).

3. Theoretical Motivation

The theoretical motivation of modeling money demand in this chapter extends from the post-

Keynesian theoretical contributions with their variation. This school of thought developed theories that are based on explicit motives for holding money, taking into consideration its key functions. These are summarized in money acting as (1) medium of exchange function which leads to transaction models that consider a transaction cost for shifting from using money to alternative assets; (2) a store-of-value function which gives rise to asset or portfolio

.11 models where money is held as part of the portfolio of assets of the individuals. P 10F

Baumol (1952) and Tobin (1956) proposed that the medium-of-exchange function of money imposes viewing money as an inventory that is held for transactions purposes. The proposition indicates that holding money rather than investing in -alternative yield bearing financial assets is subject to a transaction cost. Later, Lucas (1980) developed the cash-in-advance theoretical framework to illustrate the medium-of-exchange function of money through the imposition of many constraints on production and consumption. This

10 See IMF article IV consultation report for Egypt ; various issues; accessible at http://www.imf.org/external/country/egy/ 11 Sriram (2001) provides an exhaustive literature review of theories of money demand models and extensions; transaction models for money demand are based on contributions by Barro and Fischer (1976) and Cuthbertson and Barlow (1991). Roley (1985) lists the theoretical work done by Clower and Howitt (1978), Akerlof (1979), Akerlof and Milboume (1980a), and Santomero and Seater (1981), among others, as alternatives to the transactions demand approaches of Baumol (1952) and Tobin (1956) and Smith (1986).

19 was later developed—by the Yale School—to analyze the store of value function of money within an asset or portfolio approach. Under this approach, the demand for money is evaluated in the context of a portfolio choice problem. Under this approach, money is seen as a component of many other financial assets within a portfolio (see Tobin, 1958) and (Judd and Scadding, 1982). These assets differ in yields’ and risks’ characteristics.

The Cambridge School later analyzed demand for money within a consumer’s utility maximization problem. The utility function includes money along with other assets within a portfolio; and solving the utility maximization problem provides the optimal portfolio composition (see Friedman, 1956 and Barnett, 1980).

The takeaway of this brief survey of key theoretical underpinnings is to develop a solid setup for a money demand function. We can confidently portray money demand as a function of interest rate and a scale variable—money income is a usual proxy for that (see

Hendry, 1993)—in addition to an appropriate opportunity cost for holding money (Sriram

2001), this is represented as follows:

= f (S, OC) (1)

Where:

: Real Money Balances S: Scale Variable

OC: Opportunity Cost for Holding Money

Laidler (1993) defined the opportunity cost for holding money as the money’s own rate of return and the return on assets that are substitutes for money in a portfolio balance outset shown above. While the own rate of return on M1 is zero by definition of it being

20 the sum of currency in circulation, demand deposits and checking accounts. Yet the own rate of M2 is the prevailing interest rate on deposits since it includes interest bearing accounts. On the other hand, yield on alternative assets is defined by the return rate on financial and real assets other than money. The expected inflation rate is considered the relevant proxy for this variable, particularly in less developed financial systems that are over regulated and suffer government control (Friedman (1956) and Hendry and Starr

(1988)). Accordingly, the above formulation is rewritten as:

= (, , ) (2)

Where:

: Real Money Balances : : () :

Taking the logarithm of the variables under consideration would entail interpreting the estimated coefficients as elasticities. However, literature recommended that some variables are to be considered in level form to be interpreted as slopes rather than elasticities. Friedman and Schwartz (1982) emphasized that interest rates should be considered in levels since theory favors a constant slope rather than constant elasticity.

While Friedman and Schwartz (1982) also indicated that the inflation rate is also to be considered in levels since it might be negative, Hendry and Ericsson (1990) introduced the first difference of logs of price levels. The regression equation therefore reads as follows:

− = ∝ +∝ +∝ +∝ +∝ + (3)

Where:

: Logarithm of Real Money Balances

− : Logarithm of :

: () :

In open economies, money demand is influenced by foreign asset holdings. The portfolio

balance approach imposes agents that decide on the balance of foreign and domestic asset

holdings. This is influenced by the opportunity cost of holding each class of these assets.

Kamin and Ericsson (1993) conclude that inflation is a significant variable when

modeling money demand and currency substitution. In this case, currency substitution is

referred to by altering portfolio holdings of domestic assets for foreign assets due to

volatility of exchange rates. Foreign interest rates are also considered a variable of

interest if investment decisions and portfolio allocations involve investment in foreign

securities within open economies. As discussed earlier with regards to domestic interest

rates, the foreign interest rate enters the regression equation in levels as well as

depreciation, since it might take negative values.

The open economy’s money demand regression equation reads:

− = ∝+∝ +∝ +∝ +∝ +∝ +∝ ∆ + (4)

Where:

22 : Logarithm of Real Money Balances

− : Logarithm of :

: :

: ∆ : ℎ

4. Hypothesis to be Tested

In view of the theoretical motivation, we can represent the general model in the following specification:

12 M2tP11F P = f (gdp RtR,,Interest Rate RtR, Discount Rate RtR, Inflation RtR, Exchange Rate RtR)

(+) (+/-) (-) (+/-) (-)

(5)

The null hypothesis that demand for real broad money (M2 in Egypt positively H ) relates to real GDP and/or real consumption as portrayed by theory will be tested.

However, when it comes to price levels, some ambiguity emerges depending on the position of the economies within the business cycle and the prevailing level of inflation rate. When inflation is high, households will tend to spend more and keep less money on hand to avoid loss of value, pointing to a negative relationship between inflation and real narrow money. On the other hand, if inflation is relatively low, then households will keep

12 Expected signs between brackets based on theory. Keynesian theory identified three motives for holding real money balances and liquidity preference including transactions, precautionary and speculative motives. The rationale behind the later was questioned by several authors paving the way for more emphasis on transactions demand (Baumol, 1952; Tobin, 1956) and the asset motive (Tobin, 1958; Friedman, 1956).

23 more money on hand assuming—under static expectations—that it is better to keep more money on hand to meet any transaction costs. Accordingly, the relation will be positive in this case.

Moreover, broad money balances that have a negative relationship with depreciation of

13 the exchange rate P12F dueP to portfolio reallocation will also be tested.

This is in addition to testing the hypothesis whether speculation motive reflects negatively on a relationship between money demand and interest rates.

The null hypothesis of a unit income elasticity of money demand will also be tested. This will test the theoretical proposition of the QTM that the volume of produced goods and services and the money supply will grow at the same speed. Rejecting this null will provide evidence that inefficiencies and rigidities related to costs of holding money and transaction motive for money demand holds for the economy under consideration and provides guidance to policy on the need for further innovation and development of the financial system (Knell and Stix 2005).

Another null hypothesis that will be tested is whether the spread between the deposit rate and the interest rate is statistically not different from zero. The discount rate reflects the interest rate that the Central Bank of Egypt charges commercial, depository banks for loans to meet temporary shortages of funds. It can be seen as a channel to influence the

13 Based on the definition of: value in national currency per unit of foreign currency

24 financial market’s liquidity. This was a primary monetary policy instrument in Egypt

14 until the interbank system was introduced in 2001. P13F

A significant foundation of QTM proposes that money supply growth is followed by equal changes in the inflation rate and, by the force of the Fisher effect, in the nominal interest rate. A hypothesis test of a one-to-one relationship between price level and monetary aggregate will test whether this proposition holds for Egypt.

5. The Variable Selection and Description of Data i. Variable Selection

The analysis employs annual data covering the period from1959 until 2013. Data has been obtained from the IMF’s International Financial Statistics (IFS) database, and was last accessed in January 2015. The data series that was obtained included the following:

• Monetary Aggregates: this is defined as nominal local currency in circulation in

addition to demand deposits and time deposits (M2) deflated by the CPI with base

year 2010 in logarithms.

• Scale Variable: This is a measure of activity of the real sector that reflects

transaction demand for money. The volume of transactions is greater as the output

grows. The scale variable will be represented as real GDP deflated by the CPI

with a base year of 2010 in logarithms. The real consumption will also be

14 Please see Central Bank of Egypt’s Economic Review Vol. 50. No.3, 2009-10, available at www.cbe.org.eg

25 employed to assess the feasibility of both variables and identify the most

appropriate scale variable for modeling the demand for money in Egypt. This

15 dissertation will assume absence of money illusion following Tobin (1972). P14F P This

means that people will not behave differently when they receive payoff

information in real or nominal terms. Dropping this assumption is beyond the

scope of this dissertation.

• Price Variable: The price variable is included to represent yields on real assets as

alternatives to holding money. This will reflect on individuals' preferences of

portfolio holdings. The choice of the price variable has been constrained by data

availability and the degree of the openness of the economy (Sriram 1999). The

literature has shown flexibility using a GDP deflator, a GNP deflator, and a

wholesale price index in addition to the consumer price index. The Egyptian

economy has been suffering from chronic inflation problems. Accordingly,

inflation should be included as an opportunity cost for real assets in Egypt. This

dissertation will use CPI to compute inflation as a price variable since it is the best

available consistent series that can serve as a price deflator. This is defined as the

year on year percentage growth in the CPI with base year 2010 (not in

logarithms). It is worth noting that many earlier studies employed inflation as one

of the price variables including Ericsson and Sharma (1996), Ericson, Campos

and Tran (1990), and Johansen (1992).

15 Tobin argues that “an economic theorist can, of course, commit no greater crime than to assume money illusion”.

26 • Opportunity Cost for Holding Money: (1) Own rate of return is represented by

the three months’ deposit rate (not in logarithms). This is driven by the fact that

quasi money’s share of M2 jumped from 12 percent on average in the 1960s to

read almost 50 percent by the mid-1980s, then it dominated M2 calling for around

70 percent on average during the 1990s and 2000s. Three months’ deposit rate is

the main preference for banks’ clientele as it contributes to around 55 percent of

banking sector’s deposits. (2) The discount rate should be considered also as an

opportunity cost for M2 under the proposed model structure (not in logarithms).

This is driven by the fact that monetary authorities relied on it as an operational

monetary policy instrument since the 1950s until 2006 when it was linked to the

treasury bills’ rate as the domestic debt market became more active.

• Foreign Interest Rate: The foreign interest rate is included to proxy for capital

mobility in an open economy setup. Under conditions of perfect capital mobility,

the uncovered interest rate parity condition holds, i.e. the domestic interest rate is

equal to the foreign interest rate plus the expected depreciation of the domestic

currency. Under market imperfections and capital controls, the foreign interest

rate provides a proxy for opportunity cost for holding money. Since the US is the

largest trading partner and foreign investor in Egypt and due to data availability,

the US federal funds’ rate will be incorporated as a non-modeled variable under

the open economy scenario for modeling money demand (not in logarithms).

• Expected Exchange Rate Depreciation: This accounts for the effect of external

shocks on money demand in Egypt. The small open economies are usually

vulnerable to external balance of payment shocks that in turn trickle down to

27 affect propensity of holding domestic currency under a portfolio balance

approach. The current model will consider the change of the logarithm of the

nominal exchange rate for this purpose.

Note that while the monetary aggregate and the scale variable (variables a and b) are considered in the logarithm format, the price variable, opportunity cost, and foreign interest rates (variables c, d, and e) are not included in logarithms. This setup of a semi- log model implies that the estimated slope coefficients for variables c, d, and e are interpreted as semi-elasticities of real money relevant to these three variables. This measures the relative change in real money for a given absolute change in the value of these three explanatory variables at time (t).

Moreover, while theory considers expected inflation as a proxy for price variable and the expected exchange rate depreciation, this dissertation will adopt a simplistic approach that assumes rational expectations and perfect foresight that allows exchange rate at time

(t) to serve as a proxy for the expected exchange rate at time (t+1). ii. Descriptive Analysis of Key Variables

As expected, graph 1 indicates that GDP shows an upward trend during the sample space.

Visual inspection of a log of real GDP indicates that the Egyptian economy grew in real terms at an increasing rate during 1960–1990, where it suffered a slow down during

1990–1995, which was the result of adoption of the stabilization reform program. The economy reverted back to increasing growth rates during 2000–2009 and slowed down during the Arab Spring years 2010–2013.

28 Yet, a log of real money balances showed a turbulent uptrend. This, in turn, confirms that money demand in Egypt plays a significant role in responding to propagations of business cycles. The opportunity cost of holding money had been decreasing at an increasing rate during the 1960s, which is pertinent to the Suez war decade. The decline rate slowed down in the 1970s, reflecting a relatively higher opportunity cost for holding money during this period that witnessed the enactment of the open door policy. This is when

Egypt liberalized its capital account. The decline rate was accelerating until the mid-

1990s when Egypt adopted a stabilization program to control growing inflation that accompanied the open door policies. The program adopted liquidity supply control as the main tool for controlling inflation and accelerating real economic growth. With this motivation, the following section conducts an investigation of the presence of a long term relationship among money, GDP, consumption, and nominal interest rates.

During the past five decades, the Egyptian economy surfed through many economic and political events that reflected on monetary aggregates, output and, accordingly, demand for money. What follows is a brief account on the key events that were captured by the model:

1976: Huge foreign capital inflows between 1974 and 1985 due to number of factors; the rise in oil prices which had a positive impact on Egypt because of the increase in oil production, the reopening of Suez Canal, the surge in workers’ remittances, and the inflow of foreign aid. The 1975 banking law, interest rates were fully determined by the

Central Bank during this period and the lending and borrowing rates were negative.

29 1986: The exchange rate was devalued by almost 60 percent, and a flexible exchange rate system was announced for commercial banks to pool foreign exchange resources.

Security trouble due to security soldiers’ assaults pushed inflation to almost 23 percent which established a 14-year record.

1991 and 1992: Egypt signed a standby agreement with the IMF in 1990 that called for economic reform and structural adjustment known as the Economic Reform and

Structural Adjustment Program (ERSAP). The set of reforms that was endorsed and implemented included:

• Removal of interest ceilings on lending

• Liberalization of the exchange rate; foreign exchange bureaux were licensed to

operate freely, and the official rate devalued by around 25 percent.

• CBE adopted domestic liquidity (M2) as an intermediate target, while the

operational target became banks’ excess reserves.

• CBE also reduced the reserve ratio to 15 percent and introduced some changes to

its method of calculation

2008: The global food crises erupted coupled with a sharp outflow of capital, as foreign investors pulled out of equity and government debt markets. This created pressures on the exchange rate market. This coupled with an increase in headline inflation was driven largely by fruit and vegetable prices. Monetary authorities allowed some additional nominal exchange rate flexibility, with the Egyptian pound depreciating by about 6 percent during this year.

30 2009: The global financial crisis sparked in the US and spread to Europe and the rest of the world. As a result, Egypt witnessed a setback in the growth rate of real GDP from 7 percent in 2007/08 to 4.7 percent in 2008/09. The financial crisis hit the revenues from tourism, exports, workers’ remittances, and the Suez Canal. In addition, foreign direct investments declined. Nevertheless, sustained and wide-ranging reforms since 2004 reduced fiscal, monetary, and external vulnerabilities and in addition to limited direct exposure to structured products and low levels of international financial integration.

Government undertook additional (mainly infrastructure) expenditure of about 1 percent of GDP.

2010: Egypt weathered the financial crisis relatively well. The economy started to recover as a result of prompt fiscal and monetary responses to the financial crisis. Real

GDP growth rate stood at 5 percent.

2011: The first wave of the revolution erupted followed by the fall of the political regime and the ruling military council to govern a transition. Egypt has experienced a drastic fall in both foreign investment and tourism revenues, followed by a 60 percent drop in foreign exchange reserves, a 3 percent drop in growth, and a rapid devaluation of the

Egyptian pound. All of which led to increasing food prices, worsening unemployment rates, and shortage of fuel.

2012: The Muslim brotherhood leader was elected as president, political tensions continued and Egypt suffered an economic collapse. The economy suffered large fiscal deficits, rising public debt, fragility in the balance of payments and, hence, losses of foreign exchange reserves. Growth has been only 2 percent on average, and the

31 unemployment rate has risen to over 13 percent. Exchange rate pressures were particularly strong throughout 2012 and the first half of 2013, when reserves were only supported by sizable official financing from Gulf countries, rapid depreciation, and foreign exchange rationing, which compressed imports and reactivated the parallel exchange market.

2013: The second wave of the revolution erupted and the rule of the Muslim brotherhood ended. Egypt is still suffering an economic crisis. Inflation picked up 10.1 percent on average in 2013 due to fuel and tobacco price hikes and increases in school tuition fees.

The aim of the interest rate action by the CBE was to contain inflation hikes by raising its policy rates by 100 bps.

6. Econometric Methodology and Modeling Strategy

Inspecting the stochastic properties of the variables is the first step in analyzing the relationship between the variables. This procedure involves visual inspection of variables to check whether series are trending smoothly. The smooth trending pattern makes it feasible to quantitatively test for the presence of unit roots following the Augmented

Dickey Fuller procedures (ADF) (see Dickey and Fuller 1979, Campbell and Perron

1991, and Enders 1995). This step is vital as it determines the strategy and methodology for modeling. Using variables with unit roots in linear regression violates the basic assumptions needed for asymptotic analysis and consistency of estimation as it leads to

spurious results (Davidson and Mackinnon, 1999). P

The unrestricted (or general) form of the ADF (1981) unit root test equation is:

32 k 3 D = + + a D + g +y +x yt b0 1yb t- 1 ∑ i y - it ∑ iCSit trend t i=1 i=1

(6) trend is time trend, x is a white noise errors

This is defined as “order of integration of the series”. Variables of interest interact to enter into equilibrium in the long run if they are integrated of the same order. This is the key objective of this dissertation that attempts to test the nature of the equilibrium relationship between the real monetary aggregates, the real sector, and the external sector.

Accordingly, a system of equations will be modeled to investigate the existence of a stable relationship between the variables of interest employing a Vector Auto Regression

16 (VAR) procedure and testing for optimal lag length using the general to specific criteria P15F P

(see Enders 1995).

This is complemented by inspecting the graph of residuals and their distribution visually, as well as residual diagnostic tests to ensure that residuals are white type and do not influence the parsimony of the model. The Chow test and break point test for model stability are also implemented in order to ensure consistency of estimation and inference.

The Johansen (1988) and Johansen and Juselius (1990) tests for co-integration are then employed upon ensuring that the series are integrated of the same order. Tests of weak

16 The optimal lag length is defined as lags are reduced without losing information.

33 exogeneity will also provide evidence on the nature of the long run equilibrium relationship between variables.

The full information maximum likelihood of a Vector Error Correction Model is as following:

k- 1 D = P + G D + m +e yt yt- 1 ∑ i y - it t i=1

(7)

y m Where, t is a (n x 1) vector of the n variables in interest, is a (n x 1) vector of

e constants, G represents a (n x (k-1)) matrix of short-run coefficients, t denotes a (n x 1) vector of white noise residuals, and P is a (n x n) coefficient matrix. If the matrix P has reduced rank (0 < r < n), it can be split into a (n x r) matrix of adjustment coefficients

b a , and a (n x r) matrix of co-integrating vectors . The former indicates the importance of the co-integration relationships in the individual equations of the system and of the speed of adjustment to disequilibrium, while the latter represents the long-term P = a b ¢ equilibrium relationship, so that , k is the maximum number of lags in VAR; t denotes time, and D is a difference operator.

Testing for co-integration, using the Johansen’s reduced rank regression approach, centers on estimating the matrix P in an unrestricted form, and then testing whether the restriction implied by the reduced rank of P can be rejected. In particular, the number of the independent co-integrating vectors depends on the rank of P which, in turn, is determined by the number of its characteristic roots that are different from zero. The test

34 for nonzero characteristic roots is conducted using Max and Trace tests statistics. It is important to note that as Johansen (2002) shows, since these tests tend to reject the null hypothesis of no co-integration in small samples; it is useful also to use the degree of freedom adjusted test statistics in the co-integration analysis.

A VECM is then determined following Johansen (1988) and Johansen and Juselius (1990).

The last step would be identifying a conditional VECM, including endogenous variables that rejected the null hypothesis of being excluded from the co-integrating relation.

This model will be tested for stability and residuals should be ensured as non-serially correlated. The analysis will move on to determining the short-run relation through running the VECM. The General to Specific Modeling Approach is applied. Note that the general idea behind this approach is: (1) first, to estimate general/unrestricted VECM with the maximum lags of the right hand side variables:

k- 1 k- 1 D =a +a + a D + a D +n xt 0 y ECMt- 1 ∑ 1i x - it ∑ 2i z - it yt i=1 i=0 (8)

Where, xt is a dependent variable, z t stands for a vector of explanatory variables,

ECM t - 1 is one lagged error correction term, the residuals from the long-run model, vRyt R–

is the residuals which are expected to be white noise. If α R yR falls within the interval of (-1

0) and is statistically significant, then it can be concluded that the variables exhibit a stable co-integrating relationship, and short-run fluctuations are corrected to the long-run equilibrium.

35 The coefficient of the lagged error correction term (α) is expected to be negative and statistically significant for the model to work and converge to the equilibrium path in the long run. This model should be tested also for stability to ensure the parsimony of the outcomes. (2) Second, the appropriate lag length will be determined based on the general to specific reduction methodology and the model will be tested also for stability and correctly define the nature of the relationship between the four variables in the short and long runs. The exclusion of statistically insignificant variables and performing the comprehensive set of autocorrelation, serial correlation, normality, heteroscedasticity, and misspecification tests, is key to reaching a more parsimonious specification for the model.

For this deliberation, Oxmetrics 6.1 will be used as the key econometric package for related quantitative modeling.

7. Empirical Results

This section analyzes time series properties of the data as well as different diagnostic tests. The series are tested for stationarity using the ADF, order of integration, co- integration between the variables, significance of the variables, and weak exogeneity.

Testing for Integration

The unit root tests lay the foundation for the VECM framework outlined above. This dissertation will adopt the ADF procedure to test for unit roots as indicated above. The test will incorporate both constant and trend to account for trend stationarity. Since the data is in annual frequency, then no seasonal adjustment is required. The lag length for the ADF test is determined based on the Schwert (1989) formula for annual data:

36 = 4 100 (9) where T is the number of observations and INT stands for integer value. Since T = 54, the lag length suggested by this criteria is 4 lags.

All tests are conducted by Oxmetrics 6.1. Tables (3a), (3b), and (3c) provide summary statistics of the ADF test. Following the Dickey and Pantula (1987) sequential approach to confirm the order of integration for each variable, unit roots are tested under the assumption that the order of integration is, at most, two. The second difference for the data is employed for this purpose and as shown in table (3a), data provide strong

17 evidence to reject the null hypothesis of non-stationarity at a 1 percent level. P16F P

This is replicated for the first difference of the series. The null hypothesis of existence of a unit root is rejected for the change in inflation at 5 percent significance level, and for the change in exchange rate at 1 percent significance level. The critical values for the change in real money balances, change in real GDP, change in the interest rate and discount rate cannot reject the null of a unit root (see table 3b). This might suggest that these variables may follow an I(2) process. However, the numerical values for the coefficients of the tests that minimize the Akaike's Information Criterion (AIC) (see

Akaike's (1973) ) lie between 0.55 and 0.02, respectively. This is numerically, far from unity and indicates that the time series are stationary in their first and second differences.

17 While the visual inspection of the series in graphs 1 and 11 confirm the absence of structural breaks; future research would consider testing for structural breaks at unknown dates and conditioning on the most recent break for estimation following El-Shazly (2015)

37 Moreover, upon replication of this procedure with levels of data, the null hypothesis can’t be rejected for all variables with the exception of the change in the nominal exchange rate. This step concludes the existence of strong evidence that all the data series robustly follow an I(1) process in levels, with the exception of the change in the exchange rate that follows an I(0). The quantitative exercise is complemented by visual inspection to double check this conclusion. As seen in graphs (1) for the closed economy specification and

(12) for the open economy specification; there are no noticeable structural breaks in the series as it is trending smoothly upward and downwards. This confirms the conclusions from the unit root tests. i. Cointegration Under Closed Economy Formulation

The standard econometric theory concludes that regressions for non-stationary variables give spurious results. The residuals of these models will not be stationary and, accordingly, the standard OLS assumptions will not hold. This applies to the Egyptian data that was tested based on the ADF procedure for integration in the previous section and found to possess unit roots and exhibits integration of first order [I(1)]. However, some linear combination of these series has a lower order of integration. There will also be some (co-integrating) vector of coefficients that forms a stationary linear combination of these variables.

The aforementioned analysis confirmed the fact that levels of all variables are integrated of the same order (I(1)). This is considered the first necessary condition needed to investigate if there is a long run relationship between the variables. The existence of an equilibrium long run relationship between the variables would mean that the variables are co-integrated. The definition of the co-integration relationship can be explained by the

38 fact that it is the set of linear restriction—in other word the linear relationship—by which a number of non-stationary—integrated—series is combined to produce a stationary series. This stationary series is then analyzed to show how variables move and adjust in the long run equilibrium.

The next step to identify the short and long term relations between the variable of interest

(real money balances is considered for this chapter) is to run a VAR in levels since the five series of the proposed were proven to be integrated of order one (I(1)). Then, it will be plausible to investigate the presence of a long run equilibrium relation between variables. One advantage of level VAR models over alternative models is that the former are robust to the number of unit roots in the system. This robustness is one of the reasons why level VAR models are used extensively in applied macroeconomic research, including our case (Qureshi, 2008).

The linear representation of the variables in the long run is often called the Granger theorem (Granger, 1983). Moreover, (Engle and Granger, 1987) proposed a methodology to map a one-to-one relationship between co-integration and a VECM. The VECM can well define the short run and the long run relationships between variables. In other words, it estimates how changes in levels of variables are matched by a change in the level of the variable of concern. These estimations are considered consistent and highly efficient.

Selecting the Lag Length - Closed Economy Formulation

The reliability of the findings of the VAR system of equations depends on consideration of the lag length in running the system. A procedure is needed to test the optimal number of lags that maximize the efficiency of the model and ensure that the feedback

39 information is adequately and efficiently maintained within the model. The optimal lag length is tested using several criteria including AIC, Shwartz Criteria (SC) and the Log

Likelihood test. The theory proposes that with annual data the propagation of the business cycle is considered within a 5-year period (Cotis and Coppe, 2005).

Analysis starts with a VAR system using 5 lags and then a sequential reduction process is implemented to move from a general to a specific model to avoid over-parameterization and ensure parsimony. The results of the F-test statistics shown in (table 4) confirm that 3 lags reject the null of loss of information upon sequential reduction of the rank of the

VAR from 5 lags to 3 lags. The SC of the 5 and 4 lags systems are much higher compared to the 3 lags. Moreover, the AIC and individual F tests for specific lags provide mixed evidence for the 5 lags system. However, evidence for the 4 lags AIC, SC, and joint significance provide strong evidence against the adoption of this lag length. The relevant systems of rank 5 and 4 either suffered from problems of serial correlation and heteroscedasticity. On another front, the set of tests calls for adopting 3 lags as optimal lag length while avoiding loss of information. Also, reduced models with 2 lags and a single lag perform worse than the 3 lags models in terms of residuals diagnostics.

Residuals consistently exhibited serial correlation that invalidated results of relevant models. Accordingly the reduced 3 lags rank model is further vetted below to validate the choice of this rank.

The residuals misspecification test of the reduced rank—3 lags—VAR is provided in

(table 6) and are also graphed in graph 2. The residuals misspecification test provides evidence that the multivariate Portmanteau test’s null hypothesis up to lag 3 is rejected.

This test inspects whether residuals exhibit white noise independent and identically

40 distributed (iid) white noise characteristics. The null hypothesis of the multivariate

Portmanteau test states that the autocorrelation functions of all series have no significant elements for lags 1 through 3, yet the main weakness of the test is that the alternative hypothesis isn’t well identified. Accordingly, while the test can identify the problem of lacking (iid), it doesn’t provide further information about the nature of the problem.

Moreover, the null hypothesis of (iid) failed to be rejected for all individual equations.

These mixed results require looking at alternative tests to ensure that the residuals are free from serial correlation that invalidates the results of the model.

Results of the system as well as individual equations AR 1-2 test’s and ARCH 1-1 test for the restricted 3 lags model null of the presence of serial autocorrelation, hence the reduced VAR doesn’t suffer from serial auto correlation. Moreover, the vector ZHetero test fails to reject the null of homoskedasticity of the system’s residuals. This is also the case for individual equations’ Hetero test. System normality tests null hypothesis is rejected for the system as well as individual equations of discount rate and interest rate at

1 percent. This is mainly attributed to a number of outliers. Upon inspection of graph 2, it can be seen that density of the residuals i close to a robust bell shape.

The restricted VAR stability and parameter constancy can be confidently concluded upon inspection of graphs 3 and 4. Model constancy is tested by the Chow one step test and the break point test indicated in graph 3, in addition to the plot of the companion matrix of the reduced rank model. The graph of the one step residuals also confirms these findings.

In conclusion, individual equation and vector misspecification tests for the reduced rank 3 lags model residuals and model stability robustly support our proposition to proceed with

41 modeling long run and short run dynamics adopting 3 lags and conditioning the analysis on white type noise residuals.

Co-integration Analysis - Closed Economy Formulation

Moving forward to the co-integration analysis—the 3 lags system is employed—and

Johansen’s co-integration approach is adopted. The constant enters the model as unrestricted and the 9 dummies elaborated earlier are also unrestricted, and accordingly don’t lie in the co-integrating space.

The presence of a co-integrating relationship is identified by Johansen’s maximum likelihood estimator approach. This approach helps in the distinction and determination of the equilibrium vectors, and can describe the whole co-integrating relationship. The test builds on building a matrix that defines the equilibrium correction relationship.

The rank of this matrix is then identified by the number of linearly independent rows or columns that define this relationship. For the purpose of our analysis, the full rank of this matrix is 5, and the matrix is 5x5, since we have a 5 equation system and we are testing for the rank(r) of the matrix that identifies the number of co-integrating relationships through computing the likelihood for various eigen values of the matrix. The first block of (table 7a) shows the results of the test, as the null that rank (r) equals zero rejects the null of the no co-integrating relationship, yet the null that (r) 1 fails to reject it. This is indicated by the p-values of the trace and maximal eigen value tests for both the asymptotic case and the finite sample case.

Results of the maximal eigenvalue (λ ) provide strong evidence of a single co- integrating relationship. The null hypothesis is rejected of no co-integration at 1 percent

42 significance level and trace eigenvalue (λ )—adjusted for the degrees of freedom— rejects the same null at 5 percent significance level. Table (7a) also reports that a maximal eigenvalue test rejects the null at 5 percent for a second co-integrating relationship. The test statistic of the trace test and the adjusted tests indicate that the null hypothesis wasn’t rejected at all significance levels. This is complemented by the fact that the eigenvalue associated with the first co-integrating vector is certainly dominant over those corresponding to other vectors, thereby confirming the existence of a unique co-integrating vector in the model.

These results support a research decision to confidently proceed with the analysis given the existence of a single co-integrating relationship for the closed economy formulation.

Long-run Modeling—Closed Economy Formulation

We run a co-integrated VAR then analyze the standardized eigenvectors. These are defined as the matrix containing the parameters of the co-integrating vectors. The rows of the β matrix—as defined in the earlier methodology section—represent the standardized coefficients of the variables entering into the respective co-integrating vector. These coefficients are then normalized to unity along the principal diagonal of this matrix.

The estimation process is then followed by a set of tests for weak exogeneity and stationarity.

Table (7d & e) shows the results of various hypothesis tests for statistical significance of beta coefficients (elasticities) in addition to the weak exogeneity tests of alphas.

The test statistics show that betas for the constant and inflation—individually and jointly— aren’t statistically different from zero at a 5 percent significance level. This indicates that the inflation rate doesn’t enter the long run equation for real broad money balances.

43 However, when the joint statistical significance for betas of constant and inflation is tested together with the alpha coefficient for real money, the test fails to reject that these three coefficients are jointly different from zero at a 5 percent significance level with χ2

(2) = 10.602. This provides ample evidence that the model converges towards equilibrium.

The individual tests for betas of all other variables reject the null hypothesis that they are excluded from the long run—co-integrating—relationship at a 5 percent significance level for real income and a 1 percent significance level for the interest rate and the discount rate.

The joint test for statistical significance of betas strongly rejects the null hypothesis at all significance levels as well giving χ2 (5) = 32.104. Therefore, the three variables (real income, opportunity cost, and real consumption) have a long run relationship to real money demand in Egypt.

Further tests of statistical significance of alphas were conducted to determine the feedback effects of the co-integrating relationship. The money demand own alpha was negative and statistically significant at a 5 percent level, hence a long run relation can be identified.

The test statistics show strong evidence to reject the null that (real income, inflation rate, and interest rate) are weakly exogenous and hence their respective alphas are significantly different from zero with a 1 percent significance level. This still holds when these three alpha coefficients are jointly tested with betas of inflation rate and the constant.

44 The joint test for statistical significance of all alphas strongly rejects the null hypothesis at a 1percent significance level giving χ2 (4) = 32.175. This in turn confirms the fact that we can’t work with a single equation framework.

Further individual and joint significance tests were conducted and summarized in table

(7e) and indicate that the alpha of discount rate isn’t statistically different from zero at 1 percent, and when tested jointly with betas of inflation rate and the constant, it can be concluded that these three coefficients are different from zero at a 5 percent significance level. Joint tests of statistical significance for the aforementioned hypothesis, together with alpha for real income, provided strong evidence that these coefficients aren’t statically equal to zero. Accordingly, real income isn’t weakly exogenous.

It can be concluded that the feedback between the real money balances, real income, the inflation rate, and the interest rate equations provides significant information needed to determine the long run relation between these variables.

The long run equilibrium relation for real money demand in Egypt under a closed economy formulation is provided in equation 10 below.

The implied long run equilibrium relationship for real money balances (rm2) can be outlined (see table 7b):

rm2 = 1.0351 * rgdp - 0.82178 * Interestrt + 0.69302* DISC

(10)

It can be seen that income homogeneity applies as the coefficient for real income is closed to unity in accord with the quantity theory of money. However, the signs of the

45 own rate of return is negative and the opportunity cost of money was positive. The coefficient represents the semi-elasticity of real balances with respect to interest rates, and its value is -0.82178 if using percentages. At a 5 percent interest rate, the elasticity of money balances with respect to interest rates is -0.04. Whereas at a 5 percent discount rate, the elasticity of real money balances is estimated to be positive and reads 0.035.

The negative long term relationship between money and interest rate is common in less developed economies. Hoffinan and Chakib (1994) explained the mixed inconclusive evidence on the response of real money balances to interest rates in less developed economies. They show that the empirical literature provides mixed evidence, as some studies show that interest rates are not significant at all, and in other cases the demand for money is elastic to interest rates. The negative relationship between real money and interest rate in the Egyptian context is interpreted in macroeconomics text books as the

“liquidity effect”. The increasing interest rates, for example, require a decrease in the stock of money. According to this interpretation, money demand is a decreasing function of the nominal interest rate because the interest rate is the opportunity cost of holding cash (liquidity). So a decrease in the supply of money must cause interest rates to increase in order to keep the money market in equilibrium.

It is worth noting that many other studies on less developed economies reached a similar conclusion on the money—interest rate negative relation. Lacayo-Anderson (2001) reported -0.45 interest rate elasticity to real money in El-Salvador. Martner and Titelman

(1993) also find a negative long run elasticity for real narrow money of - 0.61 for Chile.

Another study on real money demand in Brazil during the 1980s estimated that long-run interest rate elasticity was -0.30 (see Rossi (1989)). The state of the Egyptian economy

46 throughout the modeling period is very much closer to the state of the Chilean economy in terms of suffering chronic inflation, while El-Salvador and Brazil also experienced wars and political instability during the 1980s that featured structural issues and mismanagement of monetary and fiscal policies.

An alternative test for stationarity of rm2 was conducted through testing if beta coefficients of (real income, inflation rate, interest rate, and discount rate) are jointly different from zero. The null hypothesis was strongly rejected at a 1 percent significance level with χ2 (4) = 32.063, thereby providing strong evidence that real broad money balance is non-stationary.

Moreover, the hypothesis that the interest rate and the discount rate are statistically equal in magnitude and opposite in signs couldn’t be rejected at a 5 percent significance level and χ2 (3) = 7.1491. This provides statistical evidence that the impact of raising interest rates is offset by lowering the discount rate in a closed economy setting.

The diagnostic statistics for the VECM’s system of equations indicate that the system is free from serial correlation since the Vector (SEM-AR 1-2) test statistic F(50,99) =

1.1727 [0.2485]. It is also free from hetrokedasity since the Vector ZHetero test statistic reads F(110,97) = 1.0653 [0.3761].

However, the systems residuals as well as a single equation—with exception of real income—suffer from non-normality due to a couple of outliers that can be visually inspected in graph (5). The visual inspection of this graph also confirms that the residuals are seen to be fairly normally distributed. The individual equations are all free from serial correlation except for the difference equation for the interest rate where the AR 1-2 F-test

47 indicates the presence of serial correlation at a 5 percent significance level, yet the

ARCH1-1 test indicates the opposite.

The model was also tested for overall stability. It can be visually inspected in graph 6 that the overall model is stable and fails to reject the one step Chow test (1up) and the breakpoint test (Ndn) of stability at a 1 percent significance level. Moreover, the individual coefficients were tested for stability and it can be confirmed from graph 7 that the coefficients are fairly stable. Upon graphing the roots of the companion matrix, they all lay within the unity circle and fairly near the middle. It can be concluded confidently from the aforementioned graphical inspection of stability tests that the VECM system is stable and that it is feasible to rely on the model for inference.

Short-run Modeling—Closed Economy Formulation

Having identified the long run relationship, the analysis moves to defining the short run relation. We build on the outcomes of the weak exogeneity test that real income, inflation rate, and interest rate aren’t weakly exogenous. A short term four equation model is constructed. The short run system is built using the co-integrating vector of real money;

Drm2 = + 0.592*Drm2_1 – 0.071*Drgdp_1 – 0.00123*DINFL_1 + 0.00681*Dinterestrt_1

(SE) (0.189) (0.282) (0.00254) (0.0163)

+ 0.0248*DDISC_1 – 0.0651*Drm2_2 + 0.0632*Drgdp_2 + 0.000986*DINFL_2

(0.0133) (0.203) (0.276) (0.00212)

+ 0.0123*Dinterestrt_2 – 0.0186*DDISC_2 – 0.0398*Cia_1

(0.0148) (0.00973) (0.0169)

- 0.0754*dumm1986 – 0.266*dumm1992 – 0.177*dumm2008 – 0.153*dumm2009

(0.0579) (0.13) (0.0622) (0.0813)

+ 0.079*dumm2010 – 0.135*dumm2011 – 0.0221*dumm2012 + 0.204*dumm2013

48 (0.0756) (0.0664) (0.0626) (0.0907)

- 0.0694*dumm1991

(0.063)

Drgdp = - 0.0525*Drm2_1 + 0.156*Drgdp_1 – 0.00471*DINFL_1 – 0.0172*Dinterestrt_1

(SE) (0.116) (0.172) (0.00155) (0.00997)

+ 0.0147*DDISC_1 + 0.415*Drm2_2 – 0.028*Drgdp_2 + 0.000872*DINFL_2

(0.00811) (0.124) (0.169) (0.00129)

+ 0.003*Dinterestrt_2 – 0.012*DDISC_2 – 0.023*Cia_1 – 0.0939*dumm1986

(0.00907) (0.00595) (0.0104) (0.0354)

- 0.122*dumm1992 – 0.0564*dumm2008 – 0.0535*dumm2009

(0.0797) (0.038) (0.0497)

+ 0.0708*dumm2010 – 0.0291*dumm2011 – 0.00756*dumm2012

(0.0462) (0.0406) (0.0382)

+ 0.219*dumm2013 – 0.0278*dumm1991

(0.0554) (0.0385)

DINFL = + 9.01*Drm2_1 + 35.1*Drgdp_1 – 0.477*DINFL_1 – 1.31*Dinterestrt_1

(SE) (12.2) (18.1) (0.163) (1.05)

- 0.69*DDISC_1 + 18.7*Drm2_2 + 0.264*Drgdp_2 – 0.187*DINFL_2

(0.853) (13.1) (17.7) (0.136)

- 0.0366*Dinterestrt_2 + 0.577*DDISC_2 + 3.8*Cia_1 + 8.38*dumm1986

(0.954) (0.626) (1.09) (3.72)

+ 14.1*dumm1992 + 11.2*dumm2008 + 9.96*dumm2009 – 1.74*dumm2010

(8.38) (4) (5.23) (4.86)

+ 2.29*dumm2011 + 2.67*dumm2012 – 8.47*dumm2013 – 0.516*dumm1991

(4.27) (4.02) (5.83) (4.05)

Dinterestrt = + 3.11*Drm2_1 – 3.72*Drgdp_1 + 0.0343*DINFL_1 + 0.128*Dinterestrt_1

(SE) (1.94) (2.89) (0.026) (0.167)

+ 0.45*DDISC_1 – 4.21*Drm2_2 – 0.874*Drgdp_2 – 0.00704*DINFL_2

(0.136) (2.08) (2.83) (0.0217)

+ 0.194*Dinterestrt_2 – 0.00767*DDISC_2 – 0.479*Cia_1

(0.152) (0.0998) (0.174)

+ 0.104*dumm1986 – 4.58*dumm1992 + 0.368*dumm2008

(0.593) (1.34) (0.637)

- 2.27*dumm2009 + 0.782*dumm2010 – 0.0182*dumm2011

(0.834) (0.775) (0.681)

+ 0.0163*dumm2012 + 2.14*dumm2013 – 0.242*dumm1991

(0.641) (0.93) (0.646)

(11)

The VECM results in model (4) of (Annex I) indicate the unrestricted short term system of the four equations. Overall, the model is considered to be well identified. The diagnostic tests of the residuals indicate that equations of the real money balances and interest rate suffer non-normality. This, in turn, reflected on the non-normality of the overall system’s residuals. However, individual equations and the system are free from hetreskoedasticity at a 1 percent significance level. They are also free from serial correlation at a 1 percent level with the exception of the interest rate equation that fails to reject the AR 1-2 test of absence of serial correlation at 10 percent. However, residuals diagnostics substantially improve as we proceed to the parsimonious model. Moreover, the general unrestricted short run system is reasonably stable, as seen in graphs 6 and 7.

50 The roots of the companion matrix are comfortably within the unity circle to provide assurance that the data generating process is stationary and the model can be used for inference.

The speed of adjustment in the short run money demand equation is negative, statistically significant, and lies between zero and unity. It is therefore satisfying the needed criteria to set up a robust model that converges to equilibrium in the long run. This also translates to the fact that real money demand is above or below its long run equilibrium level.

Finally, the negative sign and the magnitude that is less than unity ensure that the money demand converges to the long run equilibrium.

Reduction of the General Model—Closed Economy Formulation

The estimation of the short term system with unrestricted constant and dummies indicated that a number of variables are statistically insignificant. Since our empirical analysis reached a general statistical model that captures the key characteristics of the underlying dataset, we proceed to reducing the complexity of the general model by eliminating statistically insignificant variables and checking the validity of the reductions at every stage to ensure congruence and parsimony of the finally selected model (see Hoover and

Perez (1999) and Campos, Ericsson, and Hendry (2005)).

Throughout the reduction process, 62 regressors were eliminated based on statistical insignificance. Throughout the process, a progress test was conducted to test for the validity of the elimination in each step. This test is built on the likelihood ratio of the restricted model (after reduction) versus the unrestricted model (before reduction)

Moreover, an encompassing test was conducted to ensure that no critical information was

51 18 lost throughout the reduction process. P17F P . The parsimonious model was accordingly formulated based on minimizing the Schwartz (SC) and Hannan-Quinn (AIC) information criterion based on which it was found to encompass the general unrestricted system as well as all the reduced systems. However, the proposed parsimonious system didn’t minimize the Akaike information criteria. This system reads:

Drm2 = + 0.679*Drm2_1 – 0.016*Cia_1 – 0.115*dumm2008

(SE) (0.0836) (0.00678) (0.0455)

Drgdp = - 0.00456*DINFL_1 – 0.0222*Dinterestrt_1 + 0.459*Drm2_2

(SE) (0.000871) (0.00565) (0.0603)

- 0.0167*Cia_1 – 0.0604*dumm1986 + 0.127*dumm2013

(0.00429) (0.0273) (0.027)

DINFL = + 32.5*Drgdp_1 – 0.377*DINFL_1 + 15.4*Drm2_2 + 2.19*Cia_1

(SE) (10.3) (0.106) (7.05) (0.476)

+ 7.19*dumm1986 + 7.17*dumm2008

(3.29) (3.26)

Dinterestrt = + 2.68*Drm2_1 – 4.21*Drgdp_1 + 0.0293*DINFL_1 + 0.551*DDISC_1

(SE) (1.57) (2.27) (0.0173) (0.093)

- 3.9*Drm2_2 + 0.184*Dinterestrt_2 – 0.497*Cia_1

(1.44) (0.111) (0.109)

- 5.41*dumm1992 – 2.68*dumm2009 + 0.991*dumm2010

(0.931) (0.649) (0.575)

+ 2.82*dumm2013

(0.653)

18 Full details are available upon request.

52 (12)

The residuals diagnostics of the parsimonious system indicate that the individual equations, in addition to the whole system’s residuals, are free from serial correlation and hetroskedasticity. Residuals of all individual equations are also normally distributed, with the exception of the interest rate equation. This influenced the normality of the system’s residuals that reject the null hypothesis of the normality test. Moreover, the visual inspection of the residuals diagnostic graphs showing the plots of actual and fitted values, cross plot of actual and fitted values, and scaled residuals show close fits, and the residuals aren’t seen to exhibit a pattern; despite the presence of several outliers. (see graph 8). The visual inspection of the residual correlograms, residual density, residual histogram, and residual distribution reconfirm the proposition of absence of serial correlation with distributions close to the normal bell shape.

Moreover, the parsimonious system is remarkably stable. Visual inspection of the Chow one step test and the break point Chow test at a 1 percent significance level. Also, the roots companion matrix lies within unity circle. Accordingly, the model converges in the long term. In addition to each of the four individual equations, a one step residuals test shows remarkable stability within the 1 percent confidence interval (see stability test graphs 9 and 10). This provides additional confidence to the predictive power of the model and robustness of related findings and analysis.

Key Economic Findings of the Short Run Parsimonious Model—Closed Economy Formulation

The short run equation for the real money balances in the parsimonious indicates that short run movements of money demand varies positively with the lagged change in

53 money and negatively with the error correction term. Accordingly, the demand for money in the current period influences the demand in the next period. The stability of the short run money demand requires a pulse dummy for the year 2008. This pulse dummy captures a 12 percent decline in the change in demand for real balances. As earlier discussed, this marks the year of the global financial crises. It is evident that the public lost trust in national currency during this year, influenced by the potential impact of the crises on the domestic economy.

It worth noting that the government launched a stimulus package that reached almost 5 percent of GDP to revitalize the economy; this, coupled with a hike in public spending and inflation rates. The money adjustment to a 1 percent deviation from disequilibrium is around 1.6 percent. This is a bit higher than the CBE’s 2010 study that read 1.3 percent for monthly data covering the years 1990–2010. Yet the later was under an open economy formulation.

The growth in GDP varies positively with the change in real money two periods ago, and negatively with the previous period’s interest rate. If the change in inflation rate is 5 percent, the change in output would drop by around 0.001. In case the interest rate is 10 percent, the change in real income will drop by 0.002. The change in real income would be 0.45 if the money grows by 1 percent.

The change in the current period’s’ inflation varies positively with the growth in real money balances two periods ago and the previous period’s income. Yet inflation changes negatively with the change in the previous period’s inflation. The change in inflation by 1

54 percent in the previous period lowers the change in inflation by 0.03 percent in the current period.

The change in the interest rate in the current period is responsive to lagged changes in real money and inflation. It also changes negatively with the real money change two periods ago and lagged change in both real income and the discount rate. A rise in the change in inflation by 5 percent in the previous period leads to a 1 percent increase in the change in interest rates. The impact of a 1 percent increase in the discount rate leads to a

0.5 percent increase in the change of interest rate. Accordingly, the monetary authority can indirectly influence the interest rate in the short run through a raise in the discount rate. The change interest rate was negatively affected by the 1992 economic reform program, and the 2009 response to the global crises. However, the first wave of the revolution in 2010 and the second wave in 2013 reflected on a positive change in the interest rates.

The closed economy formulation provides a stable model for demand for real money balances in Egypt. In the long run, money income homogeneity holds and the real money balances change negatively with the interest rate and positively with the discount rate.

Inflation doesn’t influence real money demand in the long term.

We can’t statistically reject that the interest rate and the discount rate operate in opposite directions in the long term. This is a significant and new contribution to the policy guidance that wasn’t tested in earlier research. Since interest rates have been liberalized since the mid-1970s, the monetary authority can use the discount rate to influence interest rates in the long term.

55 The speed of real money demand adjustment to equilibrium, using low frequency data in the proposed model, is a bit larger than high frequency data covering a shorter horizon.

The matrix below provides a summary for the short term interaction between variables in a VECM under a closed economy setting. The change in real money balances two periods earlier influences positively the change in current real income and inflation. The change in the previous period’s real money balance positively effects the change in the current period’s change in real money balances and interest rate.

Independent Variables

Drm2_ Drm2_ Drgdp_ Drgdp_ DINFL_ DINFL_ Dinterest_ Dinterest_ DDISC_ 1 2 1 2 1 2 1 2 1 Dependent Variables Variables Dependent

Drm2 +

Drgdp + - -

DINFL + + -

+ - - + + + Dinterest

While these findings are robust and in line with theoretical underpinnings, they still miss the fact that Egypt is a small open economy that is subject to the influence of foreign developments in the global economy. Moreover, the need to include a pulse dummy to the co-integrating space of the short run money demand equation, that captures the

56 impact of the 2008 crises, supports the need to consider re-estimating the money demand function to account for the external interaction with the global economy. ii. Cointegration Under Open Economy Formulation

The open economy formulation adopts equation 4, which was detailed earlier. This specification employs the change in the exchange rate and foreign interest rate to account for interaction with the global economy. Since the US is the largest trading partner and foreign investor in Egypt, the nominal US dollar exchange rate against the Egyptian pound is considered. The change in the equivalent in Egyptian currency to 1 USD is included. On another front, the US federal funds’ rate is employed as the foreign interest rate to account for the foreign opportunity cost for holding domestic currency for the period of study. Since

Egypt is a small economy relative to the US, it is therefore understood that the Egyptian monetary policy will be influenced by the stance of monetary policy, but won’t affect it reciprocally. Against this priori, the federal funds’ rate enters the model as a non-modeled variable. The VAR model is formulated with 5 lags and reduced by 1 lag at a time till reaching only 1 lag. Dynamic tests for lag length confirm that 3 lags are optimal. Moreover, the 3 lags VAR is stable and free from auto correlation at a 1 percent significance level for individual equations and the system at large. The portmanteau test rejects the null that residuals are white noise at 1 percent for the system at large; yet; individual equations confirm that residuals are white noise and fail to reject this null. The vector test is annulled for the proposed formulation as the model contains unmodeled variables and lagged endogenous variables (see Doornik 2012).

Residual diagnostics show that individual VAR equations are free from heteroskedasticty, yet the system fails to reject the null of no heterskedasticy only at a 10 percent

57 significance level. Residuals reject the null of normality driven by the outliers in the sample period that covers more than 50 years of annual data.

The VAR system is remarkably stable, as confirmed by the visual inspection of the recursive stability tests (see graphs 13,14, and 15). Accordingly, a research decision is taken to proceed with the co-integration analysis to test for long term relationships between variables.

Co-integration Analysis—Open Economy Formulation

The same set of procedures adopted under the closed economy formulation is repeated while augmenting the system with the expected devaluation in domestic currency and the foreign interest rate.

The open economy model formulation in equation (4) is adopted to pursue a co- integration analysis. The Johansen (1988) and Johansen and Juselius (1990) procedure is applied to the following set of proposed endogenous variables: rgdp, Interestrt, DISC,

Dexrtend, INFL whereas FFUNDSRT is included as a non-modeled exogenous variable.

Pulse dummies dumm1976, dumm1986, dumm1990, dumm2008, dumm2009, and dumm2013 are also added to capture policy switches. However, many dummies that were employed under the closed economy formulation is not included here because we explicitly introduce two variables that represent the foreign influence on demand for money, mainly FFUNDSRT and Dexrtend. The estimation is started with 5 lags and is repeated by removing 1 lag at a time until the lag length reaches 1. The model progress identified the 1 with 3 lags as the most appropriate. The test identifies a single unique co- integration vector that appears to be stationary significant at a 99 percent level by both

58 the trace and maximal eigenvalue criteria, even when they are adjusted for the degrees of freedom. However, results of the trace test show the possibility of the existence of multiple at the 95 percent significant level. A research decision was made to proceed with considering a single co-integrating relationship based on the following findings: (1) the difference between the eigenvalue of the first rank in comparison to the eigenvalues; (2) considering the adjusted test statistics for both the trace and maximal tests (see table 7a).

The large difference between the eigenvalue of first rank versus other ranks (almost double), in addition to the results of the adjusted trace and maximal tests failing to reject the null of no co-integrating relationship of ranks above (1), support the proposition to proceed with the analysis considering a unique co-integrating relationship.

Long-run Modeling—Open Economy Formulation

The long run equilibrium relationship for real money balances under the open economy formulation:

rm2 = 1.2990 * rgdp – 1.2506* Interestrt + 1.0733 * DISC – 8.5118* Dexrtend – 0.13232 * INFL

(13)

It is evident that the open-economy formulation provides additional information regarding the nature of the demand for real money. This formulation revealed that interest rates and devaluation have a significant and negative impact on the demand for real money in the long term. The inflation also enters the long run equilibrium relationship for real money under the open economy formulation, and has a negative relationship with money demand. The signs of all variables in the long run relationship are in line with

59 theory. Tests for significance of individual betas show that the constant and federal funds’ rates are individually and jointly insignificant statistically.

The income elasticity is larger than that of the closed economy and bears the correct sign.

A unit change income leads to a 1.3 increase in the demand for real money balances.

The interest rate and the discount rate enter the long term relationship with the same signs as in the closed economy formulation, yet with a larger magnitude for coefficients. This makes perfect sense as it reflects the influence of interacting with the outer world on the monetary system.

The long term relationship accounts for the impact of devaluation of the Egyptian pound against the US dollar on money demand. It shows that the rate of devaluation of the official exchange rate has a serious effect on the public’s trust in the national currency and, accordingly, on the demand for money. When devaluation changes by 1 percent, this leads to a drop in demand for money by 8 percent. Accordingly, a decision of devaluation of the official exchange rate leads to abandoning trust in the national currency. This provides strong emphasis on the importance of currency substitution in the economy as economic agents determine the motives of demand for money balances and decide on a portfolio composition of their holdings.

It is worth noting that incorporating the parallel exchange rate didn’t provide meaningful results at the VAR formulation, nor did it influence the long term relationship.

Accordingly, it was decided to introduce it in the short run formulation to capture the impact of the foreign exchange market imperfection on money demand in the short term.

60 While the inflation rate had no impact on the demand for money in the closed economy formulation, it enters the long term equation for real money under the open economy formulation. The interpretation of the semi log coefficient of inflation shows that at a 10 percent inflation rate, the money demand decreases by 1.3 percent.

The open economy formulation is free from serial correlation and heteroskedasticity at the individual equation level as well as for the entire system. The diagnostics tests for residuals also indicate that the hypothesis of normality is rejected at the 5 percent level for equations of money and discount rate, and the whole system. This was explained earlier to have been caused by a couple of outlier observations. The visual inspection of the distribution of residuals shows a well-defined bell shape. This provides more comfort regarding the normality of the distribution of residuals and the reliability of the inference.

The VECM is also stable, as confirmed by the visual inspection of the recursive Chow step test and the Chow break point test. The one step residuals stability test also confirms the stability of the model (see graphs 17, 18 and 19).

Short-run Unrestricted Modeling—Open Economy Formulation

The obtained short term system includes three equations. This is slightly different than the closed economy formulation. The introduction of the exchange rate and the federal funds’ rate as proxy for openness introduced a modified short term formulation.

Tests of weak exogeneity indicated that income and the discount rates are endogenous in the short term, together with real money.

61 Under an open economy, the inflation and interest rates are found to be weakly exogenous since the adjustment coefficient of both variables was found statistically insignificant at a 5 percent significance level reading Chi^2 (1) 1.9441 [0.1632] and

3.2193 [0.0728], respectively.

This makes more sense within a developing economy’s perspective since the monetary authority decides and announces the interest rates and the economy receives that as given.

Moreover, a weak exogeneity of inflation in the money demand variable space implies that money demand equations should not be considered as price equations (MacKinnon and Milbourne, 1988). The implied intuition is that there is no feedback effect for disturbances from the steady-state of the money demand function that can enter into a dynamic equilibrium relationship with inflation. Accordingly, domestic inflation is determined by factors beyond those lying in the money demand variable space.

The unrestricted short run system was built using the co-integrating vector of real money, which is interpreted as follows:

Drm2 = + 0.663*Drm2_1 - 0.5*Drgdp_1 + 0.0187*DInterestrt_1 + 0.00274*DDISC_1

(SE) (0.177) (0.278) (0.0141) (0.00722)

+ 0.296*DDexrtend_1 + 0.000805*DINFL_1 + 0.11*Drm2_2 - 0.109*Drgdp_2

(0.0899) (0.00273) (0.219) (0.243)

+ 0.0121*DInterestrt_2 + 0.00129*DDISC_2 + 0.218*DDexrtend_2

(0.0136) (0.00811) (0.0745)

+ 0.00286*DINFL_2 - 0.0344*CIa_1 + 0.0521*dumm1976 - 0.0532*dumm1986

(0.00187) (0.00883) (0.0555) (0.0526)

+ 0.129*dumm1990 - 0.187*dumm2008 - 0.133*dumm2009 + 0.142*dumm2013

(0.0581) (0.0563) (0.066) (0.0615)

Drgdp = + 0.0112*Drm2_1 - 0.019*Drgdp_1 - 0.00749*DInterestrt_1

(SE) (0.0923) (0.144) (0.00732)

+ 0.00585*DDISC_1 + 0.129*DDexrtend_1 - 0.00281*DINFL_1

(0.00376) (0.0468) (0.00142)

+ 0.364*Drm2_2 - 0.0939*Drgdp_2 + 0.0115*DInterestrt_2

(0.114) (0.127) (0.00706)

- 0.00521*DDISC_2 + 0.0963*DDexrtend_2 + 0.00226*DINFL_2

(0.00422) (0.0388) (0.000973)

- 0.0225*CIa_1 + 0.0741*dumm1976 - 0.0832*dumm1986 + 0.11*dumm1990

(0.0046) (0.0289) (0.0274) (0.0303)

- 0.068*dumm2008 - 0.0604*dumm2009 + 0.191*dumm2013

(0.0293) (0.0344) (0.032)

DDISC = + 4.71*Drm2_1 + 8.13*Drgdp_1 - 0.255*DInterestrt_1 + 0.00161*DDISC_1

(SE) (3.33) (5.21) (0.264) (0.136)

- 1.28*DDexrtend_1 - 0.0532*DINFL_1 - 2.25*Drm2_2 + 4.71*Drgdp_2

(1.69) (0.0513) (4.12) (4.57)

- 0.0565*DInterestrt_2 - 0.055*DDISC_2 + 0.697*DDexrtend_2

(0.255) (0.152) (1.4)

+ 0.0119*DINFL_2 + 0.577*CIa_1 + 0.399*dumm1976 - 0.677*dumm1986

(0.0351) (0.166) (1.04) (0.988)

- 1.54*dumm1990 + 2.89*dumm2008 - 0.169*dumm2009 - 0.969*dumm2013

(1.09) (1.06) (1.24) (1.16)

(14)

The VECM results in model (3) of (Annex IV) indicate the unrestricted short term system of the three equations. The diagnostic tests of residuals indicate that equations of the real money balances and discount rate experience nonnormality at a 5 percent significance

63 level. The same applies to the overall system’s residuals. However, individual equations and the system are free from hetreskoedasticity and serial correlation at a 1 percent significance level. This is a remarkable improvement over the closed economy formulation. This system is also stable, as seen in graphs 18 and 19. The roots of the companion matrix are comfortably within the unity circle, which provides assurance that the data generating process is stationary and the model can be used for inference.

The speed of adjustment in the short run money demand equation is negative, statistically significant, and lies between zero and unity. However, the magnitude of the speed of adjustment reads (-0.03) under the open economy, slightly lower than the closed economy reading (-0.04), yet, it still satisfies the criteria for a robust, well specified model that converges to equilibrium in the long run.

Short-run Parsimonious Modeling—Open Economy Formulation

The reduction process proposed by Hoover and Perez (1999) and Campos, Ericsson, and

Hendry (2005) was adopted, as explained earlier in the closed economy formulation. This reflected on the exclusion of 40 insignificant regressors.

The exchange rate premium was also found to interpret the dynamics of real money balances as well as the real GDP growth. It was added to both equations to achieve parsimony of the model. The premium is calculated as the difference between the black market exchange rate and the official exchange rate. It is a proxy for market imperfection.

It indicates that the larger the gap the higher level of capital controls and inability of the official exchange market to for domestic currency conversion to foreign currency.

Accordingly, individuals and businesses resort to unofficial channels to acquire foreign

64 currency. Based on statistical significance, the lag of the first difference of the exchange rate premium is included in the money demand short term equation, whereas the

19 contemporaneous first difference was included in the income short term equation. P18F P

The progress—log likelihood ratio—test for validity of restrictions, as well as the encompassing test, ensure that the parsimonious model is well specified and encompasses the general unrestricted model. The parsimonious system does minimize the AIC,

Schwartz criterion (SC) and Hannan-Quinn criterion (HQ). Accordingly, it is able to better explain the short term dynamics of money demand.

Moreover, the parsimonious system is stable as per the Chow one step test and the break point chow test at a 1 percent significance level. Also, the roots companion matrix lies within the unity circle. The plot of roots of the companion converges in the long term. In addition to each of the four individual equations, the one step residuals test showed remarkable stability within the 1 percent confidence interval (see stability test graphs 18 and 19). We can therefore conclude that this system’s predictive power is strong and relevant findings and analysis are reliable.

Key Economic Findings of the Short Run Parsimonious Model—Open Economy Formulation

The short term real money demand varies positively with its own lagged values. The increase in lagged money demand by 10 percent leads to an 8 percent increase in money

19 Earlier literature by Beljer (1978) indicate that the demand for money is significantly reduced when expectations of black-market depreciation intensify in Brazil, Chile, and Colombia. Hassan, Choudhury, and Waheeduzzaman (1995) suggest that depreciation in black market exchange rate exerts a significant negative impact on the domestic demand for money in Nigeria, Bahmani-Oskooee and Tanku (2006) find that out of 25 LDCs some LDCs, the black market rate enters into the formulation of the demand for money, in some others the official rate is the determinant. The black market premium also played a role in some countries and Tanku (2011) introduced the black market premium deirectly to money demand in 8 emerging economies.

65 demand in the current period. Moreover, in the short term, an increase of 5 percent in the interest rate in the previous period leads to a 1 percent increase in contemporaneous demand for real money.

The money adjustment to a 1 percent deviation from disequilibrium is approximately 1 percent. This is a little slower than the closed economy formulation. It also indicates that economic openness makes an adjustment to shocks and reverting to the steady state more challenging.

The demand for real money increases with the higher devaluation of the official exchange rate in the previous period. This is against the Mundell conjecture that devaluation of national currency leads to a lower demand for money. One interpretation could be attributed to the fact that the devaluation of the official rate usually couples with devaluation in the parallel exchange market. Accordingly, the public would want to maximize returns and seek arbitrage opportunities through resorting to the parallel market.

Moreover, the devaluation of the official exchange rate in Egypt is accompanied by extreme administrative controls to minimize speculation over foreign currency through the official financial system. The monetary authorities’ resort ceilings on an individual’s foreign currency purchases from banks and prioritizes the allocation of foreign exchange resources to cover critical imports. This is further explained by the negative relationship between the change in the exchange rate premium in the previous period and demand for real money in the current period. The increase in the premium by 1 percent leads to a decline of 8 percent in real money demand. The altered sign for official exchange rate devaluation can be viewed therefore within the context of portfolio adjustment.

66 Higher inflation also reflects on loss of trust in domestic currency as stipulated in theory.

The estimation in the short term real money demand equation shows a decline of 8 percent in real money demand for a 10 percent increase in the inflation rate.

The growth in real income was positively affected by the 2013 revolution. The 2013 dummy captures a rise of 0.14 percent in real income growth. Moreover, real income adjusts by 1.3 percent in case of a 1 percent deviation off equilibrium.

The real income’s response to a change in inflation and real money is in line with theory.

It responds negatively to the increase in lagged inflation and positively to the increase in real money balances two periods ago.

Finally, within an open economy setup, devaluation of domestic currency in the official exchange rate reflects positively on real income growth. The magnitude of the devaluation through the parallel exchange rate channel is weaker compared to the official exchange rate. This makes perfect sense as most of the exports, remittances flows, and

Suez Canal proceeds (accounting to 70 percent of foreign currency income) are routed through the formal financial system, which adopts the official exchange rate in the current account conversion; whereas the tourism and services sector might resort to the parallel exchange rate market

Independent Variables

DDexrt Dexrt Dexrt DDexrt DInterest DINFL Drm2_1 Drm2_2 DINFL _1 end_1 prmium prmium_1 end_1 rt_1 Dependent Variables Drm2 + + - + -

Drgdp + + + - -

67 The following matrix summarizes the short term dynamics within the system:

The discount rate responds positively to the lagged real money balances. It can also be perceived as a shock absorber to the 1991 IMF structural adjustment program that lead to raising the average discount rate by 4 percent as part of the monetary tightening plan and fighting dollarization. It was hiked by 3 percent in 2008 to defend the domestic currency and rebuild trust in the Egyptian pound in the aftermath of the global financial crises.

Finally, the model captures a drop of 2.7 percent in 2012 as a reaction to the attempt to relax monetary policy to expedite a post 2011 revolution economic recovery. It is also seen as a source of disequilibrium in the system and a deviation of 0.24 percent off the stay state for a 1 percent increase in the discount rate.

8. Conclusion

The money demand equation for Egypt is stable under both closed and open economy formulations. The monetary authority can resort to monetary aggregates to achieve monetary policy objectives and adjust for long run growth in the real economy.

The money income elasticity under an open economy formulation remained within acceptable norms of 1.3 and is statistically not equal to unity. This is due to the fact that the increase in real income in the economy is coupled with an uptrend in monetization as the ratio of money stock over output is uptrending.

One key finding of this research confirms that the adjustment of real money to disequilibrium in the short term is slow. This might be attributed to structural factors related to financial underdevelopment and stagnant wages and prices. The Egyptian

68 economy has undergone massive transformation during the past five decades, yet exhibited low employment levels and strong inflation inertia. Moreover, domestic inflation has a weakly exogenous characteristic in the money demand space. Combining this with a slow adjustment of money suggests the presence of a ratchet effect. The finding of a ratchet effect suggests that the disequilibrium might become a new equilibrium as individuals adapt to operating with lower money holdings. Accordingly, inflation influences money demand in the medium and long terms.

The expected depreciation rate of the domestic currency against the US dollar remains a significant opportunity cost for holding money. This reveals that currency substitution is a determinant factor for motivating money holdings. The higher rate of devaluation reflects on an accelerated drop in money demand in the long term. Moreover, devaluation within the parallel market is negatively related to the change in demand for real money balances in the short term. Individuals hold more domestic currency if the official exchanges rate drops and arbitrage opportunities are sought in the parallel market.

Annex I: Summary of Findings of the Research on Estimating Money Demand

for Egypt

70 Summary of Findings of the Research on Estimating Money Demand for Egypt:

Data Properties Modeling Approach

Sample and Frequency Type Author (year) Source Variables Integration Specification Estimates

Scobie 1983 1947 - annual GNP, Money NA Structural LM2; LGNP_t, LM2 = 0.1 + 0.38* LGNP_t + 0.04 LGNP_t-1 – 1981 Balances and model LGNP_t-1, 0.86* LCPI_t + 0.2 LCPI_t-1, 0.66* LM2_t-1 – domestic LCPI_t, 0.1 LM2_t-2 price levels LCPI_t-1, LM2_t-1, LM2_t-2

Handy 1998 1973 - annual Currency M2X, ADL three models, CUR M2X M2 1997 (CUR), Inflation, CUR, M2X, Domestic offshore M2: Liquidity interest are (M2X all I (1) excluding foreign Independent currency deposits), variables: Total liquidity (M2), RGDP, CPI, Inflation, Interest, Offshore interest, RGDP, 1.07* 1.86* 1.69* Dummy 1985-1992 CPI, 0.55* 0.625* 0.653*

Inf, -0.22* -0.15* -0.13*

interest rate, 0.004 0.010* 0.019*

offshore 0.012* -0.0010 0.00394* interest rate

71 Data Properties Modeling Approach

Sample and Frequency Type Author (year) Source Variables Integration Specification Estimates

Alami 2001 1980 I – Quarterly, (F) Ratio of F, LE, ECM 10 models: In this study, quarterly data for Egypt covering 1993 IV DP foreign LEPST are the period 1981:IV to 1993:IV is represents a currency I (1) all 1) F = LE + pulse dummy denominated others were LDIF used to analyze the current episode of variable such deposits to I(0) dollarization. As expected, given that that DP = 1 total liquidity, IMF - at 1991:II (X) expected 2) F = LE IFS and zero change in foreign currency deposits in Egypt earn a exchange rate, 3) F = DEP + competitive rate of return, using the

otherwise , (i*-i) interest LDIF DL rate coefficient of the expected rate of depreciation represents a differential, 4) F = DEP (LE, DEP or LESPT ) as a measure level dummy (FCD) total holdings of variable such 5) F = LE SPT of currency substitution, the results of the error foreign that DL = 1 + LDIF correction models reported in for all t > currency 1991:II and deposits in local banks, 6) F = LESPT The study suggest that currency substitution is zero essentially absent in the short-run in otherwise, (FCDA) ~ in and DT banks abroad, represents a M2, (LE) log estimates of Egyptian foreign currency of tertiary denominated deposits held in Egyptian economy slope dummy 7) F = DEP + exchange rate, variable such PBV that DT = 1, (LESPT) ~ to 2,...,9 (since official 8) F = PBV the number exchange rate of post break observations 9) F = LESPT is 9) for all t + PBV beginning 10) F = LDIF 1991:II and zero otherwise if there is an increase in the slope of the trend, or DT

would take a decreasing

72 Data Properties Modeling Approach

Sample and Frequency Type Author (year) Source Variables Integration Specification Estimates

number beginning at the break, (DT = 9, 8,...,1) if there is

a decrease in the slope of the trend.

Baliamoune-Lutz and Haughton 2004 1960 - Annual GDP, M1, M2 I (1) VECM Velocity of Long run co-integration relationship: 1999 money (v) is a function of v M1 (t) =- 2.225 + Log v + 55 sam (t-1) – 0.004 anticipated (sam) and sum (t-1) unanticipated (sum) money growth for either M1 or v M2 Velocity (t) = 1.3417 + Log v - 33 sam (t- M2 1) – 0.110 sum (t-1)

Parsimonious short run model: Where M1(t) or M2(t) is regressed on D Log (v)(M1) (t) = 0.008 – 0.047 ECM + 0.358 constant and M1(t-i) and a D Log v (t-1) - 0.064 D Log v (t-2) + 0.199 D trend, Log v (t-3) – 0.296 D sam (t-1) + 4.807 D sam

(t-2) + 2.043 D sam (t-3) + 0.0005 D sum (t-1) –

(sam) is 0.002 D sum (t-2) - 0.006 D sum (t-3) defined as the estimated M1 or M2 and the D Log (v)(M2) (t) = 0.047 – 0.212 ECM + 0.341 (sum) is the difference

73 Data Properties Modeling Approach

Sample and Frequency Type Author (year) Source Variables Integration Specification Estimates

between actual D Log v (t-1) - 0.540 D Log v (t-2) + 0.578 D and estimated M1 or M2. Log v (t-3) + 0.778 D Log v (t-4) + 0.391 D Log

v (t-5) – 1.067 D sam (t-1) - 1.802 D sam (t-2) +

1.504 D sam (t-3) + 2.979 D sam (t-4)+ 0.544 D

sam (t-5) +0.016 D sum (t-1) – 0.008 D sum (t-

2) - 0.003 D sum (t-3) - 0.023 D sum (t-4) -

0.025 D sum (t-5)

CBE 2010 1997m7 Monthly – RGDP (as All are I(0) VECM LRM2; Long run co-integration relationship: - GDP was scale except; LRGDP_SA, 2009m6 interpolated variable), 3 LTBILL, INF LRM2 = 0.92 rgdp - 0.69 LTBILL – 3.09 INF – based on months RGDP and 5.39 monthly deposit rate RM2 are electric (return rate I(1) energy for holding production money); using uni- opportunity Parsimonious short run model: variate cost vector Dalton includes: 3 ∆LM2t=-0.013*[LM2t-1 – 0.91 LGDP_SAt-1 + method and months 0.69 LTBILL t-1 + 3.09 INFt-1 – 5.39] +0.026 ∆ seasonally treasury bills LTBILLt-4+0.022∆INFt-2 – 0.034∆ INFt-6 – adjusted by rate, exchange 0.020∆ INFt-8+0.005 multiplicative rate X-11 depreciation, technique inflation rate

Awad 2010 1995Q1- Quarterly, real money ln_Yt and ADL (RM2)t = b1 + Variable Coefficient t-stat Prob. 2007Q4 GDP balances, ln_Et are b2 Yt + b3 Et+ 1995Q1- (ln_RM2t ), b4 Rt + b5 C 3.3065 2.9401 0.005 2007Q4 are integrated

74 Data Properties Modeling Approach

Sample and Frequency Type Author (year) Source Variables Integration Specification Estimates

not available real GDP of order CPIt-1 + et . for Egypt (ln_Yt ), FX one, i.e. I~ 8.2304 IMF - either in this rate ( ln_Et ), (1), both Rt LN_Y 0.70235 0.000 IFS source or any three-month and EPt other source,

we used deposit rates are statistical (Rt ) and integrated methods to lagged CPI- of order LN_E 0.4470 11.209 0.000 extrapolate inflation rate zero, i.e., I~ quarterly data (0). from the -4.4905 annual data for the period R -0.0400 0.000 of 1995- 2007. -2.3769 EP -0.0105 0.021

1.9020 AR(1) 0.2763 0.063

Adjusted R-squared 0.991511

Akaike info criterion -4.376875

Durbin-Watson stat. 2.031932 Schwarz criterion -4.147432

F-statistic 1145.690

Prob (F-statistic) 0.000000

Annex II: Summary Tables and Integration Tests for Money Demand Model

Table (1): Data Description for Money Demand Model:

Acronym Description Source Adjusted Date Accessed

Monetary Aggregate: IMF Log of Real Broad Money Yes International th rm2 (including money in January 25 P P 2015 Financial circulation, demand and Statistics (log of levels less) time saving deposits)

Scale Variable (1): Yes IMF International th Rgdp January 25 P P 2015 Log of real Gross Financial (log of levels deflated Domestic Product; proxy Statistics by CPI: base year for wealth 2010)

IMF Price Variable: International th INFL (Inflation Rate: annual No January 25 P P 2015 Financial change in CPI) Statistics

IMF Opportunity Cost for International th Interestrt Holding Money (1): No January 25 P P 2015 Financial Three months deposit rate Statistics

IMF Yes Opportunity Cost for International th Disc Holding Money (2): The January 25 P P 2015 Financial discount rate (log household Statistics consumption)

No Scale Variable (2): IMF International th Rcons (log of levels deflated January 25 P P 2015 Log of real household Financial by CPI: base year consumption Statistics 2010)

Expected Exchange Rate IMF Yes Depreciation: of International th Dexrtend January 25 P P 2015 differenced log of official Financial (differenced log of nominal exchange rate Statistics levels)

IMF Foreign Interest Rate: International th FFUNDSRT No January 25 P P 2015 US Federal Funds Rate Financial Statistics

77 Table (2): Summary Statistics:

Sample: 1959 – 2013 (55 observations; annual data) # of Variables: 8

Variable Leading sample #obs #miss Minimum Mean Maximum std.dev

rm2 1959 to 2013 54 0 3.6784 5.5563 6.9803 1.0718

rgdp 1959 to 2013 54 0 4.7337 6.0125 7.2877 0.73388

INFL 1959 to 2013 54 0 -3.0000 9.0673 23.860 6.5032

Interestrt 1959 to 2013 54 0 3.0000 7.6578 12.000 2.8304

Disc 1959 to 2013 54 0 3.0000 9.3560 20.000 4.0601

rcons 1959 to 2013 54 0 4.3882 5.6571 7.0969 0.77611

Dexrtend 1959 to 2013 54 0 -0.097638 0.054311 0.59784 0.14879

FFUNDSRT 1959 to 2013 54 0 0.10000 5.4071 16.380 3.4471

Table (3a): Augmented Dickey-Fuller Tests for Unit Roots: Second Differences of Log Levels of Real Data - Sample 1959 - 2013

Variable t-adf beta Y_lag t-DY_lag Maximum Lags AIC

DDrm2 -9.329** -0.26068 0 -5.425

DDrgdp -5.181** -1.7868 1.628 4 -6.148

DDINFL -14.87** -0.62725 0 3.749

DDInterestrt -6.885** -1.5104 1.621 2 -3.656

DDDisc -5.882** -2.0901 1.623 3 0.9403

DDrcons -5.475** -2.6611 1.525 4 -5.548

DDexrtend -6.073** -3.4492 1.739 4 -3.156

Constant and Trend Included - Critical Values; T=55; 5%=-3.49 1%=-4.13

P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

78 Table (3b): Augmented Dickey-Fuller Tests for Unit Roots: First Differences of Log Levels of Real Data - Sample 1959 - 2013

Variable t-adf beta Y_lag t-DY_lag Maximum Lags AIC

Drm2 -2.659 0.55287 -0.03925 4 -5.492

Drgdp -2.977 0.38232 -0.9231 4 -6.265

DINFL -3.810* -0.85657 0.4357 4 3.169

DInterestrt -3.467 -0.12022 0.3913 4 -3.838

DDisc -3.393 -0.18911 0.1345 4 0.7395

Drcons -2.879 0.024227 0.4270 4 -5.682

DDexrtend -4.909** -1.3961 0.2195 4 -3.524

Constant and Trend Included - Critical Values; T=55; 5%=-3.49 1%=-4.13

P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

Table (3c): Augmented Dickey-Fuller Tests for Unit Roots: Levels of Real Data - Sample 1959 - 2013

Variable t-adf beta Y_lag t-DY_lag Maximum Lags AIC

rm2 -1.956 0.93461 0.9578 2 -5.636

rgdp -2.198 0.87358 1.337 2 -6.341

INFL -1.693 0.80466 0.8232 4 3.115

Interestrt -1.714 0.92756 0.2542 4 -0.7156

Disc -0.5471 0.96391 -0.6173 4 0.7336

rcons -2.302 0.76491 0.1102 4 -5.783

Dexrtend -3.731* 0.086784 0.8326 4 -3.747

Constant and Trend Included - Critical Values; T=55; 5%=-3.49 1%=-4.13

P-values are in brackets;

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

Annex III: Diagnostic Tables for Closed Economy Formulation

80 Table (4): Baseline Closed Economy Money Demand Model Lag Structure and Reduction Tests, Sample 1959 - 2013

Significance of Each Lag Joint AIC SC F Test significanc Restricted e of all lags 5 4 3 2 1 Lags

2.8685 0.77681 1.2612 1.7419 8.7956 2.8685 3.329 F(25,94) 5 [0.0001**] [0.7607] [0.2109] [0.0297]* [0.0000]** [0.0001]** 8.073

1.0083 0.85883 1.7294 8.7966 1.0083 4.519 8.352 F(25,112) 4 [0.4631] [0.6591] [0.0282] * [0.0000]** [0.4631]

1.0739 1.7249 9.6736 1.0739 4.359 7.278 F(25,131) 3 [0.3814] [0.0264]* [0.0000]** [0.3814]

2.8582 13.112 2.8582 4.140U 6.147U F(25,150) 2 [0.0000]** [0.0000]** [0.0000]**

121.86 121.86 .NaN .NaN F(25,168) 1 [0.0000]** [0.0000]**

P-values are in brackets ; *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

The Closed Economy Model VAR system is presented as follows:

, dumm1986 + dumm1991+ , = + ∑ , + . dumm1992 + dumm2008 + dumm2009 + dumm2010 + dumm2011 + , dumm2012+ dumm2013 + Where: GDP is in Logs dummXXXX: dummy variable (year) : error term

81 Table (5): Tests on the significance of each variable

Variable F-test Value [Prob]

rm2 F(15,72) = 6.2812 [0.0000]**

rgdp F(15,72) = 6.2104 [0.0000]**

INFL F(15,72) = 3.0388 [0.0008] **

Interestrt F(15,72) = 1.5745 [0.1000]*

DISC F(15,72) = 2.1325 [0.0174]*

dumm1986 F(5,26) = 1.5760 [0.2018]

dumm1992 F(5,26) = 2.4659 [0.0589]

dumm2008 F(5,26) = 4.7243 [0.0033] **

dumm2009 F(5,26) = 3.8180 [0.0100]

dumm2010 F(5,26) = 0.40268 [0.8425]

dumm2011 F(5,26) = 2.1938 [0.0857]

dumm2012 F(5,26) = 7.0776 [0.0003] **

dumm2013 F(5,26) = 2.1997 [0.0850]

dumm1991 F(5,26) = 17.779 [0.0000] **

Constant F(5,26) = 1.7887 [0.1503]

P-values are in brackets

82 Table (6): Residual Diagnostics Tests for the - 3 Lags VAR- Closed Economy Money Demand Model

*** Significance level at 1%; ** Significance level at 5%; * Significance level at 10%

Single-equation diagnostics using reduced-form residuals: rm2 : Portmanteau( 7): Chi^2(4) = 7.5270 [0.1105] rm2 : AR 1-2 test: F(2,28) = 0.32520 [0.7251] rm2 : ARCH 1-1 test: F(1,53) = 2.2940 [0.1358] rm2 : Normality test: Chi^2(2) = 0.32008 [0.8521] rm2 : Hetero test: F(30,15) = 1.5193 [0.1974] rm2 : Hetero-X test: not enough observations rgdp : Portmanteau( 7): Chi^2(4) = 6.4001 [0.1712] rgdp : AR 1-2 test: F(2,28) = 0.37855 [0.6883] rgdp : ARCH 1-1 test: F(1,53) = 0.78215 [0.3805] rgdp : Normality test: Chi^2(2) = 1.1438 [0.5644] rgdp : Hetero test: F(30,15) = 2.8876 [0.0167]* rgdp : Hetero-X test: not enough observations INFL : Portmanteau( 7): Chi^2(4) = 3.5036 [0.4773] INFL : AR 1-2 test: F(2,28) = 1.2216 [0.3100] INFL : ARCH 1-1 test: F(1,53) =0.00096847 [0.9753] INFL : Normality test: Chi^2(2) = 1.8518 [0.3962] INFL : Hetero test: F(30,15) = 0.67094 [0.8287] INFL : Hetero-X test: not enough observations Interestrt : Portmanteau( 7): Chi^2(4) = 7.8441 [0.0975] Interestrt : AR 1-2 test: F(2,28) = 4.3289 [0.0230]* Interestrt : ARCH 1-1 test: F(1,53) =0.00044828 [0.9832] Interestrt : Normality test: Chi^2(2) = 42.311 [0.0000]** Interestrt : Hetero test: F(30,15) = 0.58780 [0.8949] Interestrt : Hetero-X test: not enough observations DISC : Portmanteau( 7): Chi^2(4) = 7.5435 [0.1098] DISC : AR 1-2 test: F(2,28) = 1.1853 [0.3205] DISC : ARCH 1-1 test: F(1,53) = 0.021263 [0.8846] DISC : Normality test: Chi^2(2) = 17.203 [0.0002]** DISC : Hetero test: F(30,15) = 0.85468 [0.6554] DISC : Hetero-X test: not enough observations

83 Vector Portmanteau( 7): Chi^2(100)= 164.09 [0.0001]** Vector AR 1-2 test: F(50,76) = 1.0344 [0.4409] Vector Normality test: Chi^2(10) = 44.957 [0.0000]** Vector ZHetero test: F(150,59) = 1.1354 [0.2926] ZHetero-X test: not enough observations

84 Table 7(a): Baseline - 3 Lags VAR- Closed Economy Money Demand Model

Cointegration Analysis with Johansen Test: sample 1959–2013

eigenvalue Log likihood Trace test Max test Trace test Max test rank for rank [ Prob] [ Prob] (T-nm) (T-nm)

109.70 52.21 79.78 37.97 0 4.707021 [0.000]** [0.000]** [0.029]* [0.017]*

57.49 24.16 41.81 17.57 1 0.61296 30.81082 [0.023]* [0.171] [0.388] [0.622]

33.33 15.28 24.24 11.12 2 0.35550 42.89089 [0.077] [0.365] [0.452] [0.740]

18.05 12.20 13.12 8.88 3 0.24261 50.53262 [0.098] [0.180] [0.362] [0.459]

5.84 5.84 4.25 4.25 4 0.19900 56.63454 [0.210] [0.210] [0.388] [0.387]

5 0.10078 59.55572

P-values are in brackets, *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

85 Table 7(b): Eigen vector of the variables entering into the respective cointegrating vector

beta (scaled on diagonal; cointegrating vectors in columns

rm2 1.0000 -0.47695 165.25 21.856 -6.0917

rgdp -1.2264 1.0000 -259.85 -30.087 8.5706

INFL 0.013214 0.024104 1.0000 -1.2279 -0.086809

Interestrt 0.37234 0.00087224 -17.559 1.0000 -1.3580

DISC -0.35275 -0.074257 7.6098 -0.41313 1.0000

Constant 1.7232 -2.6453 695.39 71.806 -15.638

Alpha

rm2 -0.088507 0.18350 -0.00024601 0.0010006 0.0032835

rgdp -0.038323 0.082962 0.00037222 0.0013230 -0.00055115

INFL 7.6654 -5.7450 -0.0025741 0.014953 0.67741

Interestrt -0.83995 -0.20789 0.0060341 -0.030286 0.047328

DISC 0.77753 1.3702 0.0060106 -0.026798 0.028332

Table 7(c): Reduced Rank Standardized Coefficients (with one covariance)

Beta Vector Std Err Alpha Vector Std Err

rm2 1.0000 0.00000 -0.041544 0.016867

rgdp -1.0351 0.084268 -0.025441 0.010118

INFL 0.00000 0.00000 3.7152 1.0861

Interestrt 0.82178 0.15918 -0.59177 0.14166

DISC -0.69302 0.10365 0.00000 0.00000

Constant 0.00000 0.00000

86 Table 7(d): Hypotheses Tests for the Beta Vector

rm2 Zero Chi^2(1) 5.6395 [0.0176]*

rgdp Zero Chi^2(1) 3.9278 [0.0475]*

INFL Zero Chi^2(1) 1.5497 [0.2132]

Interestrt Zero Chi^2(1) 25.429 [0.0000]**

DISC Zero Chi^2(1) 24.227 [0.0000]**

Constant Zero Chi^2(1) 1.4148 [0.2343]

Joint Significance for betas (including Zero Chi^2(5) 32.104 [0.0000]** constant)

32.063 [0.0000]** Chi^2(4) Joint Significance for betas (excluding Zero constant) [strong evidence that rm2 is non

stationary]

Joint Significance for Betas for Zero Chi^2(2) 4.9499 [0.0842] (INFL and Constant)

Joint Significance for Betas for (INFL Zero Chi^2(2) 10.602 [0.0141]* and Constant) and alpha for (rm2)

87 Table 7(e): Hypotheses Tests for the Alpha Vector: Weak Exogeneity and Joint Tests: rm2 Zero Chi^2(1) 6.1845 [0.0129]*

Rgdp Zero Chi^2(1) 3.3496 [0.0672]

INFL Zero Chi^2(1) 12.546 [0.0004]**

Interestrt Zero Chi^2(1) 6.1643 [0.0130]*

DISC Zero Chi^2(1) 4.2857 [0.0384]*

Joint Significance for alphas Zero Chi^2(4) 32.175 [0.0000]**

Joint Significance for alphas and betas Zero Chi^2(9) 50.635 [0.0000]**

Joint Significance for alpha of (rgdp), Interestrt and INFL Zero Chi^2(3) 17.956 [0.0004]**

Joint Significance for alpha of (rgdp), Interestrt, INFL and betas of Zero Chi^2(5) 33.825 [0.0000]** (INFL and Constant)

Joint Significance for alpha of (rgdp) and betas of (INFL and Constant) Zero Chi^2(3) 9.562010 [0.0277]*

Joint Significance for alpha of (DISC) and betas of (INFL and Zero Chi^2(3) 7.0710 [0.0697] Constant)

Joint Significance for alpha of (DISC), (rgdp) and betas of (INFL and Zero Chi^2(4) 13.440 [0.0093]** Constant)

Joint Significance for alphas of (DISC and rm2) and betas of (INFL Zero Chi^2(4) 13.559 [0.0088]** and Constant)

Joint Significance for alphas of (DISC, rgdp and rm2) and betas of Zero Chi^2(5) 14.599 [0.0122]* (INFL and Constant) alpha of (DISC) and betas of (INFL and Constant) are jointly zero and Chi^2(3) 7.1491 [0.0673] the betas for Interest and Disc have opposite signs

P-values are in brackets, *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

88 Table 7(f): Parsimonious/Reduced VECM – Closed Economy:

MOD(62) Estimating the model by FIML

The estimation sample is: 1959 - 2013

Equation for: Drm2 Coefficient Std.Error t-value t-prob Drm2_1 0.678687 0.08360 8.12 0.0000 CIa_1 -0.0160414 0.006776 -2.37 0.0237 dumm2008 -0.115021 0.04550 -2.53 0.0163

sigma = 0.0584738

Equation for: Drgdp Coefficient Std.Error t-value t-prob DINFL_1 -0.00456193 0.0008713 -5.24 0.0000 DInterestrt_1 -0.0221933 0.005650 -3.93 0.0004 Drm2_2 0.459160 0.06025 7.62 0.0000 CIa_1 -0.0166841 0.004290 -3.89 0.0004 dumm1986 -0.0604373 0.02730 -2.21 0.0336 dumm2013 0.126706 0.02703 4.69 0.0000

sigma = 0.0347738

Equation for: DINFL Coefficient Std.Error t-value t-prob Drgdp_1 32.5405 10.29 3.16 0.0033 DINFL_1 -0.377184 0.1063 -3.55 0.0012 Drm2_2 15.4206 7.047 2.19 0.0356 CIa_1 2.19206 0.4763 4.60 0.0001 dumm1986 7.19174 3.288 2.19 0.0357 dumm2008 7.16930 3.263 2.20 0.0349

sigma = 3.54329

Equation for: DInterestrt Coefficient Std.Error t-value t-prob Drm2_1 2.68178 1.574 1.70 0.0975 Drgdp_1 -4.20947 2.275 -1.85 0.0729 DINFL_1 0.0292526 0.01732 1.69 0.1004 DDISC_1 0.550912 0.09303 5.92 0.0000 Drm2_2 -3.90089 1.442 -2.70 0.0106 DInterestrt_2 0.183695 0.1110 1.65 0.1072 CIa_1 -0.497069 0.1093 -4.55 0.0001 dumm1992 -5.41473 0.9314 -5.81 0.0000 dumm2009 -2.67578 0.6491 -4.12 0.0002 dumm2010 0.991003 0.5750 1.72 0.0939 dumm2013 2.82036 0.6527 4.32 0.0001

sigma = 0.502194

log-likelihood 31.986552 -T/2log|Omega| 344.153029 no. of observations 55 no. of parameters 26

LR test of over-identifying restrictions: Chi^2(58) = 77.674 [0.0433]*

89 BFGS using analytical derivatives (eps1=0.0001; eps2=0.005): Strong convergence

Single-equation diagnostics using reduced-form residuals: Drm2 : AR 1-2 test: F(2,50) = 0.62435 [0.5397] Drm2 : ARCH 1-1 test: F(1,53) = 1.1254 [0.2936] Drm2 : Normality test: Chi^2(2) = 2.4337 [0.2962] Drm2 : Hetero test: F(4,50) = 0.67772 [0.6106] Drgdp : AR 1-2 test: F(2,47) = 2.0747 [0.1369] Drgdp : ARCH 1-1 test: F(1,53) = 0.17532 [0.6771] Drgdp : Normality test: Chi^2(2) = 0.064306 [0.9684] Drgdp : Hetero test: F(8,46) = 0.88164 [0.5391] DINFL : AR 1-2 test: F(2,47) = 2.4575 [0.0966] DINFL : ARCH 1-1 test: F(1,53) = 0.61288 [0.4372] DINFL : Normality test: Chi^2(2) = 0.82793 [0.6610] DINFL : Hetero test: F(8,46) = 1.5749 [0.1587] DInterestrt : AR 1-2 test: F(2,42) = 3.1256 [0.0543] DInterestrt : ARCH 1-1 test: F(1,53) = 0.087662 [0.7683] DInterestrt : Normality test: Chi^2(2) = 21.788 [0.0000]** DInterestrt : Hetero test: F(14,40) = 0.45759 [0.9423]

Vector SEM-AR 1-2 test: F(32,141) = 0.91562 [0.6010] Vector Normality test: Chi^2(8) = 28.903 [0.0003]** Vector ZHetero test: F(96,109) = 1.0398 [0.4205]

Annex IV: Graphical Representation and Diagnostics for Closed Economy

Formulation

91 Graph (1): Plot of Log of Levels and First Differences of Logs of Variables for Closed Economy VAR: 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 1980 1980 1980 1980 1980 1960 1960 1960 1960 1960 Drm2 Drm2 Drgdp DINFL DInterestrt DDISC 2 0 5 0 10 0.2 0.0 0.2 0.0 -10 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 1980 1980 1980 1980 1980 1960 1960 1960 1960 1960 rm2 rm2 rgdp INFL Interestrt DISC 6 4 7 5 0 5 5 20 10 15

92 Graph (2): Diagnostic Graphs of - 3 Lags VAR- Closed Economy VAR PACF-r:Interestrt PACF-r:INFL PACF-r:DISC PACF-r:rgdp PACF-r:rm2 5 5 5 5 5 ACF-r:rm2 ACF-r:rgdp ACF-r:INFL ACF-r:Interestrt ACF-r:DISC 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 5 5 2.5 2.5 2.5 N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) 0.0 0 0.0 0 0.0 r:rm2 r:rgdp r:INFL r:Interestrt r:DISC -2.5 Density Density Density Density Density -2.5 -2.5 -5 0.50 0.25 0.50 0.25 0.50 0.25 0.75 0.25 0.75 0.25 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 1980 1980 1980 1980 1980 r:rm2 (scaled) r:rgdp (scaled) r:INFL (scaled) r:Interestrt (scaled) r:DISC (scaled) 1960 1960 1960 1960 1960 1 1 2 0 2 0 -1 -1 2.5 -2.5 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 Fitted Fitted Fitted Fitted Fitted 1980 1980 1980 1980 1980 rm2 rgdp INFL Interestrt DISC 1960 1960 1960 1960 1960 6 4 7 5 0 5 5 20 10 15

93 Graph (3a): Scaled Residuals - 3 Lags VAR- Closed Economy VAR

0.2 0.25 r rm2 r rgdp 0.1

0.00 0.0

-0.1 -0.25 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 20 r INFL 2.5 r Interestrt

0 0.0

-2.5 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 5.0 r DISC

2.5

0.0

-2.5

1970 1980 1990 2000 2010

Graph (3b): Long Run Stability Test of - 3 Lags VAR- Closed Economy VAR

Roots of companion matrix 1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

-0.75

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

94 Graph (4): Stability Test (One Step and N Down Chow Test) of - 3 Lags VAR- Closed Economy VAR 2020 2020 2020 2020 2000 2000 2000 2000 1% 1% 1% 1% 1980 1980 1980 1980 1upINFL 1upCHOWs Ndn INFL Ndn CHOWs 1.0 0.5 0.0 1.0 0.5 1.0 0.5 0.0 1.0 0.5 2020 2020 2020 2020 2000 2000 2000 2000 1% 1% 1% 1% 1980 1980 1980 1980 1uprgdp 1upDISC Ndn rgdp Ndn DISC 1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 1.0 0.5 0.0 2020 2020 2020 2020 2000 2000 2000 2000 1% 1% 1% 1% 1980 1980 1980 1980 1uprm2 1upInterestrt Ndn rm2 Ndn Interestrt 1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 0.0

95 Graph (5): Diagnostic Graphs of Unrestricted Closed Economy VECM: PACF-r:DInterestrt PACF-r:DINFL PACF-r:DDISC PACF-r:Drgdp PACF-r:Drm2 5 5 5 5 5 ACF-r:Drm2 ACF-r:Drgdp ACF-r:DINFL ACF-r:DInterestrt ACF-r:DDISC 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 5.0 5.0 2.5 2.5 2.5 2.5 2.5 N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) 0.0 0.0 0.0 0.0 0.0 -2.5 r:Drm2 r:Drgdp r:DINFL r:DInterestrt r:DDISC -2.5 -2.5 Density Density Density Density Density -2.5 -2.5 -5.0 0.75 0.25 0.75 0.25 0.75 0.25 0.75 0.25 0.75 0.25 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 1980 1980 1980 1980 1980 r:Drm2 (scaled) r:Drgdp (scaled) r:DINFL (scaled) r:DInterestrt (scaled) r:DDISC (scaled) 1960 1960 1960 1960 1960 2 1 2 0 2 2 0 -2 -1 -2 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 Fitted Fitted Fitted Fitted Fitted 1980 1980 1980 1980 1980 Drm2 Drgdp DINFL DInterestrt DDISC 1960 1960 1960 1960 1960 2 0 5 0 10 0.2 0.0 0.2 0.0 -10

96 Graph (6): Stability (One Step and N Down Chow Test) of Unrestricted Closed Economy VECM: 2020 2020 2020 2020 2000 2000 2000 2000 1% 1% 1% 1% 1upDINFL 1upCHOWs Ndn DINFL Ndn CHOWs 1980 1980 1980 1980 1.0 0.5 1.0 0.5 1.0 0.5 1.0 0.5 2020 2020 2020 2020 2000 2000 2000 2000 1% 1% 1% 1% 1upDrgdp 1upDDISC Ndn Drgdp Ndn DDISC 1980 1980 1980 1980 1.0 0.5 1.0 0.5 1.0 0.5 1.0 0.5 2020 2020 2020 2020 1% 2000 2000 2000 2000 1% 1% 1% 1upDrm2 1upDInterestrt Ndn Drm2 Ndn DInterestrt 1980 1980 1980 1980 1.0 0.5 1.0 0.5 1.0 0.5 1.0 0.5

97 Graph (7a): Scaled Residuals of Unrestricted Closed Economy VECM:

0.50 r Drm2 0.2 r Drgdp 0.25

0.00 0.0

-0.25 -0.2 1980 1990 2000 2010 1980 1990 2000 2010 20 5.0 r DINFL r DInterestrt

10 2.5

0 0.0

-10 -2.5

1980 1990 2000 2010 1980 1990 2000 2010

r DDISC 2.5

0.0

-2.5

1980 1990 2000 2010

Graph (7b): Long Run Stability Test of Unrestricted Closed Economy VECM:

Roots of companion matrix 1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

-0.75

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

98 Graph (8): Diagnostic Graphs of Parsimonious Closed Economy VECM: PACF-r:DDISC PACF-r:Drgdp PACF-r:Drm2 5 5 5 ACF-r:Drm2 ACF-r:Drgdp ACF-r:DDISC 0 0 0 1 0 1 0 1 0 2.5 2.5 2.5 N(0,1) N(0,1) N(0,1) 0.0 0.0 0.0 r:Drm2 r:Drgdp r:DDISC -2.5 Density Density Density -2.5 -2.5 0.4 0.2 0.50 0.25 0.50 0.25 2020 2020 2020 2000 2000 2000 1980 1980 1980 r:Drm2 (scaled) r:Drgdp(scaled) r:DDISC (scaled) 1960 1960 1960 2 0 2 0 2 0 -2 -2 2020 2020 2020 2000 2000 2000 Fitted Fitted Fitted 1980 1980 1980 Drm2 Drgdp DDISC 1960 1960 1960 5 0 0.2 0.1 0.0 0.1 0.0

99 Graph (9): Stability (One Step and N Down Chow Test) of Parsimonious Closed Economy VECM: 2010 2010 2010 2010 2000 2000 2000 2000 1% 1% 1% 1% 1% 1% 1990 1990 1990 1990 1upDrgdp 1upCHOWs NdnDrgdp NdnCHOWs 1980 1980 1980 1980 1.0 0.5 0.0 1.0 0.5 1.0 0.5 1.0 0.5 2010 2010 2010 2010 2000 2000 2000 2000 1% 1% 1% 1% 1% 1% 1% 1990 1990 1990 1990 1up Drm2 1upDrm2 1upDDISC Ndn Drm2 NdnDrm2 NdnDDISC 1980 1980 1980 1980 1.0 0.5 0.0 1.0 0.5 1.0 0.5 1.0 0.5

100 Graph (10): Scaled Residuals of Parsimonious Closed Economy VECM:

0.5 r Drm2

0.0

-0.5 1980 1985 1990 1995 2000 2005 2010 2015

r Drgdp 0.25

0.00

-0.25

1980 1985 1990 1995 2000 2005 2010 2015

5 r DDISC

-5 1980 1985 1990 1995 2000 2005 2010 2015

101

Annex V: Diagnostic Tables for Open Economy Formulation

102 Table (8): Baseline Open Economy Money Demand Model Lag Structure and Reduction Tests, Sample 1959 - 2013

Significance of Each Lag Joint AIC SC F Test significanc e of all lags Restricted Lags 5 4 3 2 1

1.3603 0.65583 1.1394 1.5240 4.5069 1.3603 0.256 8.359 F(36,59) 5 [0.1449] [0.9116] [0.3226] [0.0743] [0.0000]** [0.1449]

1.1279 1.1682 1.5371 6.0894 1.1279 1.573 8.361 F(36,86) 4 [0.3196] [0.2757] [0.0544] [0.0000]** [0.3196]

1.0946 1.9038 7.3171 1.0946 1.959 7.433 F(36,112) 3 [0.3514] [0.0057]** [0.0000]** [0.3514]

1.8907 8.4633 1.8907 1.968 6.129 F(36,138) 2 [0.0048]** [0.0000]** [0.0048]**

70.426 70.426 2.410 5.257 F(36,165) 1 [0.0000]** [0.0000]**

P-values are in brackets ; *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

The Open Economy Model VAR System is presented as follows:

, , dumm1986 + = + ∑ , . + dumm1976 + , ℎ dumm1990+ dumm2008ℎ + dumm2009 , + dumm2013 + Where:

GDP and Exchange Rate are in Logs

dummXXXX: dummy variable

: error term

103 Table 9: Tests on the significance of each variable

Variable F-test Value [Prob]

rm2 F(18,71) = 7.3925 [0.0000]**

rgdp F(18, 71) = 6.3452 [0.0000]**

INFL F(18, 71) = 3.1743 [0.0003]**

Interestrt F(18, 71) = 1.7739 [0.0462]*

DISC F(18, 71) = 1.4601 [0.1007]

Dexrtend F(18, 71) = 3.2265 [0.0002]**

FFUNDSRT F(6, 25) = 1.3779 [0.2620]

dumm1976 F(6,30) = 5.4211 [0.0007]**

dumm1986 F(6, 25) = 2.5657 [0.0448]*

dumm1990 F(6, 25) = 4.0413 [0.0058]**

dumm2008 F(6, 25) = 3.7728 [0.0082]**

dumm2009 F(6, 25) = 1.0373 [0.4251]

dumm2013 F(6, 25) = 4.7097 [0.0025]**

Constant F(6, 25) = 0.78440 [0.5903]

P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

104 Table 10: Residual Diagnostics Tests for the - 3 Lags VAR- Open Economy Money Demand Model

Single-equation diagnostics using reduced-form residuals: rm2 : Portmanteau( 7): Chi^2(4) = 4.4285 [0.3511] rm2 : AR 1-2 test: F(2,27) = 1.3448 [0.2775] rm2 : ARCH 1-1 test: F(1,53) = 0.034285 [0.8538] rm2 : Normality test: Chi^2(2) = 6.3129 [0.0426]* rm2 : Hetero test: F(38,10) = 1.7061 [0.1851] rgdp : Portmanteau( 7): Chi^2(4) = 8.0445 [0.0900] rgdp : AR 1-2 test: F(2,27) = 0.82838 [0.4476] rgdp : ARCH 1-1 test: F(1,53) = 1.0172 [0.3178] rgdp : Normality test: Chi^2(2) = 0.066368 [0.9674] rgdp : Hetero test: F(38,10) = 1.2495 [0.3708] INFL : Portmanteau( 7): Chi^2(4) = 5.8932 [0.2073] INFL : AR 1-2 test: F(2,27) = 0.29108 [0.7498] INFL : ARCH 1-1 test: F(1,53) = 0.21295 [0.6464] INFL : Normality test: Chi^2(2) = 7.2488 [0.0267]* INFL : Hetero test: F(38,10) = 1.7271 [0.1794] Interestrt : Portmanteau( 7): Chi^2(4) = 7.1310 [0.1291] Interestrt : AR 1-2 test: F(2,27) = 1.0070 [0.3786] Interestrt : ARCH 1-1 test: F(1,53) = 0.11425 [0.7367] Interestrt : Normality test: Chi^2(2) = 15.172 [0.0005]** Interestrt : Hetero test: F(38,10) = 0.70863 [0.7873] DISC : Portmanteau( 7): Chi^2(4) = 8.5992 [0.0719] DISC : AR 1-2 test: F(2,27) = 2.2083 [0.1294] DISC : ARCH 1-1 test: F(1,53) =0.00045357 [0.9831] DISC : Normality test: Chi^2(2) = 5.2131 [0.0738] DISC : Hetero test: F(38,10) = 1.3958 [0.2962] Dexrtend : Portmanteau( 7): Chi^2(4) = 7.0328 [0.1342] Dexrtend : AR 1-2 test: F(2,27) = 1.1939 [0.3185] Dexrtend : ARCH 1-1 test: F(1,53) = 0.090908 [0.7642] Dexrtend : Normality test: Chi^2(2) = 37.593 [0.0000]** Dexrtend : Hetero test: F(38,10) = 2.0481 [0.1129]

Vector Portmanteau( 7): Chi^2(144)= 204.79 [0.0007]** Vector AR 1-2 test: F(72,71) = 1.2269 [0.1949] Vector Normality test: Chi^2(12) = 59.437 [0.0000]** Vector ZHetero test: F(228,38) = 1.7474 [0.0206]* Vector RESET23 test: F(72,71) = 2.2173 [0.0005]**

105 Table 11 (a): Baseline Open Economy Money Demand Model Cointegration Analysis with Johansen Test: sample 1959–2013

eigenvalue Log likihood for Trace test Max test Trace test Max test rank rank [ Prob] [ Prob] (T-nm) (T-nm)

165.81 68.82 46.30 111.55 0 30.84816 [0.013]* [0.000]** [0.000]** [0.008]**

96.99 36.22 65.25 24.37 1 0.71387 65.25920 [0.001]** [0.030]* [0.283] [0.509]

60.77 24.04 40.88 16.17 2 0.48240 83.36951 [0.010]* [0.177] [0.432] [0.732]

36.73 16.86 24.71 11.34 3 0.35405 95.38802 [0.032]* [0.251] [0.423] [0.720]

19.87 15.02 13.37 10.10 4 0.26405 103.8194 [0.055] [0.067] [0.343] [0.336]

111.3292 5 0.23897

113.7536 6 0.084384

P-values are in brackets *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

106 Table 11(b): Reduced Rank Standardized Coefficients

Beta Vector Std Err Alpha Vector Std Err

rm2 1.0000 0.00000 -0.030553 0.0079880

rgdp -1.2990 0.084212 -0.019096 0.0040082

Interestrt 1.2506 0.17615 0.00000 0.00000

DISC -1.0733 0.14192 0.70952 0.13679

Dexrtend 8.5118 1.4804 0.00000 0.00000

INFL 0.13232 0.028321 0.00000 0.00000

Constant 0.00000 0.00000

FFUNDSRT 0.00000 0.00000

Table 11(c): Hypotheses Tests for the Beta Vector

rm2 Zero Chi^2(1) 5.1698 [0.0230]*

Rgdp Zero Chi^2(1) 4.8416 [0.0278]*

Interestrt Zero Chi^2(1) 18.221 [0.0000]**

DISC Zero Chi^2(1) 25.228 [0.0000]**

Dexrtend Zero Chi^2(1) 27.786 [0.0000]**

INFL Zero Chi^2(1) 8.2673 [0.0040]**

Constant Zero Chi^2(1) 1.6581 [0.1979]

FFUNDSRT Zero Chi^2(1) 3.5538 [0.0594]

54.023 [0.0000]** strong evidence Joint Significance for beta Zero Chi^2(7) that rm2 is non stationary]

Joint Significance for FFUNDSRT and Constant Zero Chi^2(2) 4.5538 [0.1026]

P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

107 Table 11(d): Hypotheses Tests for the Alpha Vector: Weak Exogeneity and Joint Tests

rm2 Zero Chi^2(1) 14.854 [0.0001]**

Rgdp Zero Chi^2(1) 19.511 [0.0000]**

Interestrt Zero Chi^2(1) 1.9441 [0.1632]

DISC Zero Chi^2(1) 14.246 [0.0002]**

Dexrtend Zero Chi^2(1) 1.6468 [0.1994]

INFL Zero Chi^2(1) 3.2193 [0.0728]

Joint Significance for alphas Zero Chi^2(5) 56.676 [0.0000]**

Joint Significance for alphas and betas Zero Chi^2(12) 67.720 [0.0000]**

Joint Significance for alphas of (Interestrt), Zero Chi^2(3) 5.6872 [0.1279] (Dexrtend), (INFL)

Joint Significance for alphas of (Interestrt), Zero Chi^2(4) 7.6792 [0.1041] (Dexrtend), (INFL) and beta of (Constant)

Joint Significance for alphas of (Interestrt), (Dexrtend), (INFL) and beta of (Constant) and Zero Chi^2(5) 10.497 [0.0623] FFUNDSRT

Joint Significance for alphas of (Interestrt), (Dexrtend), (INFL) (rm2) and beta of (Constant) and Zero Chi^2(6) 27.040 [0.0001]** FFUNDSRT

P-values are in brackets.

*** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

108 Table 11(e): Parsimonious/Reduced VECM – Open Economy:

MOD(40) Estimating the model by FIML The dataset is: C:\Users\ahmedrostom\Dropbox\Chapter 1 Inshaa Allah\November 2014\March 2015\March 20\EGYMDJAN20151965.in7 The estimation sample is: 1959 - 2013

Equation for: Drm2 Coefficient Std.Error t-value t-prob Drm2_1 0.824396 0.08354 9.87 0.0000 DDexrtend_1 0.216645 0.04725 4.59 0.0001 CIa_1 -0.00845390 0.003614 -2.34 0.0284 DInterestrt_1 0.0200135 0.009229 2.17 0.0407 DINFL -0.00827521 0.001606 -5.15 0.0000 Dexrtprmium_1 -0.0697568 0.02559 -2.73 0.0121

sigma = 0.048086

Equation for: Drgdp Coefficient Std.Error t-value t-prob DDexrtend_1 0.138573 0.02646 5.24 0.0000 DINFL_1 -0.00358828 0.0008716 -4.12 0.0004 Drm2_2 0.449434 0.05028 8.94 0.0000 CIa_1 -0.0134603 0.002299 -5.85 0.0000 dumm2013 0.141293 0.02480 5.70 0.0000 DINFL -0.00415356 0.0009212 -4.51 0.0002 Dexrtprmium 0.0269767 0.01445 1.87 0.0747

sigma = 0.0292693

Equation for: DDISC Coefficient Std.Error t-value t-prob Drm2_1 4.77892 1.173 4.08 0.0005 CIa_1 0.243018 0.05456 4.45 0.0002 dumm1991 4.03556 0.7739 5.21 0.0000 dumm2008 2.93144 0.7183 4.08 0.0005 dumm2012 -2.77136 0.7128 -3.89 0.0007

sigma = 0.743813

log-likelihood 164.125872 -T/2log|Omega| 398.25073 no. of observations 55 no. of parameters 18

LR test of over-identifying restrictions: Chi^2(78) = 204.33 [0.0000]** BFGS using analytical derivatives (eps1=0.0001; eps2=0.005): Strong convergence

correlation of structural residuals (standard deviations on diagonal) Drm2 Drgdp DDISC Drm2 0.048086 0.48753 0.12477 Drgdp 0.48753 0.029269 0.35703 DDISC 0.12477 0.35703 0.74381

Single-equation diagnostics using reduced-form residuals: Drm2 : AR 1-2 test: F(2,47) = 2.0385 [0.1416] Drm2 : ARCH 1-1 test: F(1,53) = 0.056382 [0.8132] Drm2 : Normality test: Chi^2(2) =0.0076428 [0.9962] Drm2 : Hetero test: F(12,42) = 1.5524 [0.1440] Drgdp : AR 1-2 test: F(2,46) = 1.5290 [0.2276] Drgdp : ARCH 1-1 test: F(1,53) = 0.23856 [0.6273] Drgdp : Normality test: Chi^2(2) = 0.95285 [0.6210] Drgdp : Hetero test: F(12,42) = 0.92158 [0.5346] DDISC : AR 1-2 test: F(2,48) = 1.9985 [0.1467]

109 DDISC : ARCH 1-1 test: F(1,53) = 0.060582 [0.8065] DDISC : Normality test: Chi^2(2) = 4.4283 [0.1092] DDISC : Hetero test: F(4,50) = 1.5092 [0.2137]

Vector SEM-AR 1-2 test: F(18,116) = 1.1388 [0.3248] Vector Normality test: Chi^2(6) = 3.6767 [0.7203] Hetero test: not enough observations

Long-run matrix Pi(1)-I = Po Drm2 Drgdp DDISC CIa Drm2 -0.17560 0.00000 0.00000 -0.0084539 Drgdp 0.44943 -1.0000 0.00000 -0.013460 DDISC 4.7789 0.00000 -1.0000 0.24302 CIa -4.8888 0.00000 0.00000 -0.25181

Long-run covariance Drm2 Drgdp DDISC CIa Drm2 24.862 17.874 1.5310 -491.43 Drgdp 17.874 12.852 1.0983 -353.41 DDISC 1.5310 1.0983 0.097207 -30.140 CIa -491.43 -353.41 -30.140 9721.0

Static long run DInterestrt DDexrtend DINFL dumm1976 dumm1986 Drm2 -1.9736 -6.1318 -1.1137 0.00000 0.00000 Drgdp -1.4706 -4.6766 -0.80647 0.00000 0.00000 DDISC 1.1063 7.8774 0.061736 0.00000 0.00000 CIa 43.362 153.00 22.154 0.00000 0.00000 dumm1990 dumm2008 dumm2009 dumm2013 dumm1991 Drm2 0.00000 9.2070 0.00000 0.53708 12.675 Drgdp 0.00000 6.7122 0.00000 0.53284 9.2403 DDISC 0.00000 0.45443 0.00000 -0.14449 0.62559 CIa 0.00000 -191.25 0.00000 -11.156 -263.28 dumm2012 FFUNDSRT Dexrtprmium rcons Drm2 -8.7042 0.00000 -5.7733 0.00000 Drgdp -6.3456 0.00000 -4.0709 0.00000 DDISC -0.42961 0.00000 -0.45202 0.00000 CIa 180.80 0.00000 111.67 0.00000

Standard errors of static long run DInterestrt DDexrtend DINFL dumm1976 dumm1986 Drm2 18.368 59.643 9.9053 0.00000 0.00000 Drgdp 13.185 42.839 7.1093 0.00000 0.00000 DDISC 1.1749 3.7828 0.63463 0.00000 0.00000 CIa 362.25 1177.7 195.31 0.00000 0.00000 dumm1990 dumm2008 dumm2009 dumm2013 dumm1991 Drm2 0.00000 82.959 0.00000 4.8545 114.55 Drgdp 0.00000 59.546 0.00000 3.4866 82.230 DDISC 0.00000 5.3099 0.00000 0.31012 7.3227 CIa 0.00000 1635.8 0.00000 95.749 2259.5 dumm2012 FFUNDSRT Dexrtprmium rcons Drm2 78.670 0.00000 50.910 0.00000 Drgdp 56.474 0.00000 36.535 0.00000 DDISC 5.0290 0.00000 3.2683 0.00000 CIa 1551.8 0.00000 1003.7 0.00000

Mean-lag matrix sum pi_i: Drm2 Drgdp DDISC CIa Drm2 0.82440 0.00000 0.00000 -0.0084539 Drgdp 0.89887 0.00000 0.00000 -0.013460 DDISC 4.7789 0.00000 0.00000 0.24302 CIa -5.4726 0.00000 0.00000 0.74819

110 Eigenvalues of long-run matrix: real imag modulus -1.000 0.0000 1.000 -1.000 0.0000 1.000 -0.4205 0.0000 0.4205 -0.006870 0.0000 0.006870

I(2) matrix Gamma: Drm2 Drgdp DDISC CIa Drm2 1.0000 0.00000 0.00000 0.00000 Drgdp 0.44943 1.0000 0.00000 0.00000 DDISC 0.00000 0.00000 1.0000 0.00000 CIa -0.58382 0.00000 0.00000 1.0000

111

Annex VI: Graphical Representation and Diagnostics for Open Economy

Formulation

112 Graph (11): Plot of Levels and First Differences of Variables for Open Economy VAR 2020 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 2000 1980 1980 1980 1980 1980 1980 1960 1960 1960 1960 1960 1960 Drm2 Drm2 Drgdp DINFL DInterestrt DDISC DDexrtend 2 0 5 0 10 0.2 0.0 0.2 0.0 0.5 -10 -0.5 2020 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 2000 1980 1980 1980 1980 1980 1980 1960 1960 1960 1960 1960 1960 rm2 rm2 rgdp INFL Interestrt DISC Dexrtend 6 4 7 5 0 5 5 20 10 15 0.5 0.0

113 Graph (12): Diagnostic Graphs of - 3 Lags - Closed Economy VAR PACF-r:Dexrtend PACF-r:Dexrtend PACF-r:Interestrt PACF-r:Interestrt PACF-r:DISC PACF-r:DISC PACF-r:INFL PACF-r:INFL PACF-r:rgdp PACF-r:rgdp PACF-r:rm2 PACF-r:rm2 5 5 5 5 5 5 ACF-r:rm2 ACF-r:rm2 ACF-r:rgdp ACF-r:INFL ACF-r:Interestrt ACF-r:DISC ACF-r:Dexrtend 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 5 5.0 5 2.5 2.5 2.5 2.5 N(0,1) N(0,1) N(0,1) N(0,1) 0 N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) 0.0 0.0 0.0 0.0 0 r:rm2 r:rm2 r:rgdp r:INFL r:Interestrt r:DISC r:Dexrtend -2.5 Density Density -2.5 Density Density Density Density -2.5 -2.5 -5 0.6 0.2 0.4 0.2 0.50 0.25 0.75 0.25 0.50 0.25 0.75 0.25 2020 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 2000 1980 1980 1980 1980 1980 1980 r:rm2 (scaled) (scaled) r:rm2 (scaled) r:rgdp (scaled) r:INFL (scaled) r:Interestrt (scaled) r:DISC (scaled) r:Dexrtend 1960 1960 1960 1960 1960 1960 2 1 2 0 2 2 0 2 0 -2 -1 -2 2020 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 2000 Fitted Fitted Fitted Fitted Fitted Fitted Fitted 1980 1980 1980 1980 1980 1980 rm2 rm2 rgdp INFL Interestrt DISC Dexrtend 1960 1960 1960 1960 1960 1960 6 4 7 5 0 5 5 20 15 15 0.5 0.0

114 Graph (13a): Scaled Residuals - 3 Lags - Open Economy VAR

0.2 r rm2 r rgdp 0.1

0.0 0.0

-0.1 -0.2 1980 1990 2000 2010 1980 1990 2000 2010 10 r INFL r Interestrt 2

0 0

-2

1980 1990 2000 2010 1980 1990 2000 2010 0.5 r DISC r Dexrtend 2.5

0.0 0.0

-2.5

1980 1990 2000 2010 1980 1990 2000 2010

Graph (13b): Long Run Stability Test of - 3 Lags - Open Economy VAR

Roots of companion matrix 1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

-0.75

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

115 Graph (14): Stability (One Step and N Down Chow Test) Test of - 3 Lags - Open Economy VAR: 2020 2020 2020 2020 2000 2000 2000 2000 1% 1% 1% 1% 1up INFL 1up Dexrtend Ndn rgdp Ndn rgdp Ndn DISC 1980 1980 1980 1980 1 0 1 0 1 0 1.0 0.5 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 1% 1% 1% 1% 1% 1up rgdp 1up rgdp 1up DISC Ndn Ndn rm2 Ndn Interestrt Ndn CHOWs 1980 1980 1980 1980 1980 1 0 1 0 1 0 1 0 1.0 0.5 2020 2020 2020 2020 2020 2000 2000 2000 2000 2000 1% 1% 1% 1% 1% 1uprm2 1upInterestrt 1upCHOWs Ndn Ndn INFL Ndn Dexrtend 1980 1980 1980 1980 1980 1 0 1 0 1 0 1.0 0.5 1.0 0.5

116 Graph (15): Diagnostic Graphs of Unrestricted Open Economy VECM: PACF-r:DDISC PACF-r:Drgdp PACF-r:Drm2 5 5 5 ACF-r:Drm2 ACF-r:Drgdp ACF-r:DDISC 0 0 0 1 0 1 0 1 0 2.5 2.5 2.5 N(0,1) N(0,1) N(0,1) 0.0 0.0 0.0 r:Drm2 r:Drgdp r:DDISC -2.5 -2.5 Density Density Density -2.5 0.4 0.2 0.4 0.2 0.75 0.50 0.25 2020 2020 2020 2000 2000 2000 1980 1980 1980 r:Drm2 (scaled) r:Drgdp(scaled) r:DDISC (scaled) 1960 1960 1960 2 0 2 0 2 0 -2 -2 2020 2020 2020 2000 2000 2000 Fitted Fitted Fitted 1980 1980 1980 Drm2 Drgdp DDISC 1960 1960 1960 5 0 0.2 0.1 0.0 0.1 0.0

117 Graph (16a): Scaled Residuals of Unrestricted Open Economy VECM:

0.2 r Drm2

0.0

-0.2 1980 1985 1990 1995 2000 2005 2010 2015 0.2 r Drgdp 0.1

0.0

-0.1

1980 1985 1990 1995 2000 2005 2010 2015

r DDISC 2.5

0.0

-2.5

1980 1985 1990 1995 2000 2005 2010 2015

Graph (16b): Long Run Stability Test of Unrestricted Open Economy VECM:

Roots of companion matrix 1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

-0.75

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

118 Graph (17): Diagnostic Graphs of Parsimonious Open Economy VECM: PACF-r:DDISC PACF-r:Drgdp PACF-r:Drm2 5 5 5 ACF-r:Drm2 ACF-r:Drgdp ACF-r:DDISC 0 0 0 1 0 1 0 1 0 2.5 2.5 2.5 N(0,1) N(0,1) N(0,1) 0.0 0.0 0.0 r:Drm2 r:Drgdp r:DDISC -2.5 -2.5 Density Density Density -2.5 0.4 0.2 0.4 0.2 0.4 0.2 2020 2020 2020 2000 2000 2000 1980 1980 1980 r:Drm2 (scaled) r:Drgdp (scaled) r:DDISC (scaled) 1960 1960 1960 2 0 2 0 2 0 -2 -2 2020 2020 2020 2000 2000 2000 Fitted Fitted Fitted 1980 1980 1980 Drm2 Drgdp DDISC 1960 1960 1960 5 0 0.2 0.1 0.0 0.1 0.0

119 Graph (18): Stability (One Step and N Down Chow Test) of Parsimonious Open Economy VECM: 2010 2010 2010 2010 2000 2000 2000 2000 1% 1% 1% 1% 1% 1% 1% 1% 1990 1990 1990 1990 1upDrgdp 1upCHOWs NdnDrgdp NdnCHOWs 1980 1980 1980 1980 1.0 0.5 0.0 1.0 0.5 1.0 0.5 1.0 0.5 2010 2010 2010 2010 2000 2000 2000 2000 1% 1% 1% 1% 1% 1% 1% 1% 1990 1990 1990 1990 1up Drm2 1upDrm2 1upDDISC Ndn Drm2 NdnDrm2 NdnDDISC 1980 1980 1980 1980 1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 1.0 0.5 0.0

120 Graph (19): Scaled Residuals of Parsimonious Open Economy VECM:

0.5 r Drm2

0.0

-0.5 1980 1985 1990 1995 2000 2005 2010 2015

r Drgdp 0.25

0.00

-0.25

1980 1985 1990 1995 2000 2005 2010 2015

5 r DDISC

-5 1980 1985 1990 1995 2000 2005 2010 2015

121 References

1. Abdel-Baki, M. (2010), Alterations in monetary transmission mechanism in Egypt in the wake of the triple-F crisis, Journal of Investment management and financial innovations: vol. 7 (2), pages 217 – 227. 2. Abdel-Khalek, G., (1988), Stabilization and Adjustment Policies and Programs, WIDER Publications, World Institute for Development Economics Research, Helsinki, Finland 3. Abdel-Khalek, G., (2001), Stabilization and Adjustment in Egypt: Reform or De- Industrialization, Edward Elgar 4. Abul-Oyoun, M., (2003), The Developments of Monetary policy in Egypt and Future Outlook, Egyptian Center for Economic Studies Working Paper, 78. 5. Adrian, T. & H., Shin, (2009), Money, Liquidity, and Monetary Policy, Staff Reports 360, Federal Reserve Bank of New York. 6. Agenor, P. R, (1995), Monetary shocks and exchange rate dynamics with informal currency markets, International Review of Economics & Finance, Elsevier, vol. 4(3), pages 211-226. 7. Agenor, P. R, (2000), Monetary policy under flexible exchange rates - an introduction to inflation targeting, Policy Research Working Paper Series 2511, The World Bank. 8. Agenor, P. R. and A. Koray, (2009), Monetary Shocks and Central Bank Liquidity with Credit Market Imperfections, Centre for Growth and Business Cycle Research Discussion Paper Series 120, Economics, The Univeristy of Manchester. 9. Agenor, P. R. and L. A. Pereira da Silva, (2013), Rethinking Inflation Targeting: A Perspective from the Developing World, Centre for Growth and Business Cycle Research Discussion Paper Series 185, Economics, The Univeristy of Manchester. 10. Agenor, P. R. and Peter J. Montiel, (2006), Credit Market Imperfections and the Monetary Transmission Mechanism Part I: Fixed Exchange Rates, Centre for Growth and Business Cycle Research Discussion Paper Series 76, Economics, The University of Manchester. 11. Agenor, P. R. and P. J. Montiel, (2007), Credit Market Imperfections and the Monetary Transmission Mechanism Part II: Flexible Exchange Rates, Centre for Growth and Business Cycle Research Discussion Paper Series 87, Economics, The University of Manchester. 12. Agenor, P. R. and K. El Aynaoui, (2008), Excess Liquidity, Bank Pricing Rules, and Monetary Policy," Centre for Growth and Business Cycle Research Discussion Paper Series 105, Economics, The Univeristy of Manchester. 13. Agenor, P. R. &, N. U. Haque and P. J., Montiel, (1993), Macroeconomic effects of anticipated devaluations with informal financial markets, Journal of Development Economics, Elsevier, vol. 42(1), pages 133-153. 14. Agrawal, P. and Sahoo, P. (2009), Saving and Growth in Bangladesh, The Journal of Developing Areas, 42, Iss. 2, 89-110 15. Alami, T., (1999), Cointegration analysis of dollarization in Egypt, Economic Research Forum for the Arab Countries, Iran & Turkey, Cairo.

122 16. Al-Mashat R. and A. Billmeier, (2007), The Monetary Transmission Mechanism in Egypt, IMF Working Paper, No. 285. 17. Al-Mashat, R., (2002), Financial Sector Development and Economic Growth in Egypt 1960-1999, Global Development Network and Economic Research Forum for Arab Countries, Iran and Turkey. 18. Andrew D. Crockett and Owen J. Evans (1980), Demand for Money in Middle Eastern Countries. Staff Papers - International Monetary Fund, Vol. 27, No. 3, pp. 543-577 19. Arize, A.C. and S.S. Shwiff (1993), Cointegration, Real Exchange Rate and Modeling the Demand for Broad Money in Japan. Applied Economics 25(6): 717- 726. 20. Awad, I., (2010), The Monetary Targeting Regime in Egypt: Theoretical and Empirical Investigations, Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 150-164. 21. Bae, Y. and R. M. de Jong, (2007), Money demand function estimation by nonlinear cointegration. Journal. ofApplied Econometrics, 22: 767–793. 22. Bahmani-Oskooee, M. (2001), How stable is M2 money demand function in Japan?. Japan and the World Economy 13: 455-461. 23. Bahmani-Oskooee, M. and A. Kutan, (2010), How stable is the demand for money in emerging economies?, Applied Economics, Volume 42, Issue 26. 24. Baliamoune-Lutz, M. and J. (2004), Haughton, Velocity Effects of Increased Variability in Monetary Growth in Egypt: A Test of Friedman's Velocity Hypothesis, African Development Review, Vol. 16, No. 1, pp. 36-52. 25. Ball, L., (2012), Short Run Money Demand, Journal of Monetary Economics 59: 622–633 26. Baumol, W.J., Blackman, S.A.B., and Wolfe, E.N., (1991), Productivity and American Leadership: The Long View. Cambridge: MIT Press. 27. Bernanke, S. and I. Mihov, (1998) . Measuring Monetary Policy.” Quarterly Journal of Economics, 113:869-902. 28. Bernanke B., Laubach T., Mishkin F. and Posen A., (1999), “Inflation Targeting: Lessons from the International Experience”, Princeton University Press. 29. Bernanke, B. and A. S.Blinder, S, (1988),Credit, Money, and Aggregate Demand, American Economic Review, American Economic Association, vol. 78(2), pages 435-39, May. 30. Bernanke, Ben, Jean Boivin, and Piotr Eliasz, (2005), Measuring Monetary Policy: A Factor Augmented Vector Autoregressive (FAVAR) Approach, Quarterly Journal of Economics 120:1, 387–422. 31. Bernanke, B., Gertler, M., Gilchrist, S., (1999), The financial accelerator in a quantitative business cycle framework. In: Handbook of Macroeconomics. North- Holland, Amsterdam. 32. Bernanke, B.S. and Gertler, M. (1995), Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives Vol. 9. 33. Black, L. & R., Rosen (2007), How the Credit Channel Works: Differentiating the Bank Lending Channel and the Balance Sheet Channel, Federal Reserve Bank of Chicago, Working Paper # 13

123 34. Blanchard, O. and D Quah, (1989), The dynamic effects of aggregate demand and supply disturbances, American Economic Review, 79, pp. 655 –673 35. Boivin, J., and M. Giannoni, I. Mihov (2009), Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data, American economic review 99,1: 350. 36. Boivin, J., M., Kiley & F. Mishkin, (2010), How Has the Monetary Transmission Mechanism Evolved Over Time?, Finance and Economics Discussion Series, Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington, D.C 37. Bosworth, B., (1993), Saving and Investment in a Global Economy. Washington: Brookings Institution. 38. Campbell, J. C. and Perron, P. (1991), Pitfall and Opportunities: What Macroeconomists should know about Unit Roots, NBER Technical Working Paper # 100 39. Capasso, S. and Napolitano, O. (2012), Testing for the stability of money demand in Italy: has the euro influenced the monetary transmission mechanism?, Applied Economics, 44, 3121–33. 40. Central Bank of Egypt’s annual report, (2010). 41. Chen, H. and V., Cúrdia, (2011),The macroeconomic effects of large-scale asset purchase programs, Staff Reports 527, Federal Reserve Bank of New York. 42. Cheng, K., (2006), A VAR Analysis of Kenya's Monetary Policy Transmission Mechanism: How Does the Central Bank's REPO Rate Affect the Economy?, IMF Working Paper WP/06/300 (Washington: International Monetary Fund), 43. Choi, W.G., and S. Oh. (2003), A Demand Function with Output Uncertainty, Monetary Uncertainty, and Financial Innovations.” Journal of Money, Credit, and Banking 35, no. 5: 685–709. 44. Chow, G. C.. (1960), Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591–605. http://doi.org/10.2307/1910133 45. Christiano, L., M., Eichenbaum and CL. Evans, (1999), Monetary policy shocks: what have we learned and to what end? In Handbook of Macroeconomics, Vol. 1, ch. 2, Taylor JB, Woodford M (eds), Elsevier: Amsterdam; 65–148. 46. Čihák, M.; A. Demirgüç-Kunt; E. Feyen,; R. Levine, (2012), Benchmarking Financial Systems around the World. World Bank Policy Research Papers. 47. Ciccarelli, M., A. Maddaloni & J. Peydro, (2010), Trusting the Bankers, a New Look at the Credit Channel of Monetary Policy, European Central Bank, Working Paper Series # 1228. 48. Cochran, P., S. Call, and F. Glahe, (1999), Credit Creation or Financial Intermediation: Fractional Reserve Banking in a Growing Economy. The Quarterly Journal of Austrian Economics 2 (3): 53–64 49. Cotis, J. and J. Coppe (2005), Business Cycle Dynamics in OECD Countries: Evidence, Causes and Policy Implications. A working paper presented to Reserve Bank of Australia Economic Conference on The Changing Nature of the Business Cycle, Sydney, Australia – July 2005 50. Cúrdia, V. and M. Woodford. (2010), The Central-Bank Balance Sheet as an Instrument of Monetary Policy, Federal Reserve Bank of New York. 51. Dave, C., S. Dressler and L. Zhang, (2013), Journal of Money, Credit and Banking, Volume 45, Issue 8: 22

124 52. Davidson R. and J. MacKinnon (1999), Econometric Theory and Methods. Oxford University Press. 53. Deaton, A.S. and Paxson, C.H., (1992), Saving, Growth, and Aging in Taiwan, mimeo, Princeton University. 54. Demirguc-Kunt, A., and L. Klapper. (2012), Measuring Financial Inclusion: The Global Findex, Policy Research Working Paper 6025,World Bank, Washington, DC 55. Dhanasekaran, K., (2010), Testing of Savings-Growth Relationship in India: An Application of Cointegration and Error Correction Techniques, Journal of Applied Economics 9.3 (Jul 2010): 85-96. 56. Dickey, D. and W. Fuller (1979), Distribution of the Estimators for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Association, 74, 427-431. 57. Divisia, F., (1925), L’indice Monétaire et la Théorie de la Monnaie, Revue d’Economie Politique, pp. 980-1008. 58. Doornik, A. and F. Hendry (2009), Empirical Econometric Modeling Using PcGive, Vol. I. 59. Drake, L and K.A. Chrystal. (1994), Company-Sector Money Demand: New Evidence on the Existence of a Stable Long-un Relationship for the UK. Journal of Money, Credit and Banking 26(3): 479-494. 60. Duca, J., and Van Hoose, D., (2004), Recent developments in understanding the demand for money, Journal of Economics and Business, Volume 56, Issue 4, July – August 2004, Pages 247 –272 61. El-Erian, M. (1988), Currency Substitution in Egypt and the Yemen Arab Republic, IMF Staff Papers, Vol. 35, pp. 85-103. 62. El-Refaie, F., (2001), The Coordination of Monetary and Fiscal Policies in Egypt, Cairo: Egyptian Center for Economic Studies. Working Paper No. 54, (in Arabic), 63. El Mossallamy, M., T., Moursi , and E. Zakareya, (2007), Effect of Some Recent Changes in Egyptian Monetary Policy: Measurement and Evaluation. Middle East Business and Economic Review (MEBER), Vol. 19, No. 2. 64. Elsayed, H., (1993), Determinants of Savings in Egypt, Egypt Contemperaire, Vol. 421, pp. 127 – 156. 65. El-Shazly, A., (2015), Structural breaks and monetary dynamics: A time series analysis. Economic Modelling, Volume 53, February 2016, Pages 133-143 66. Elshazly, A. (2008), The Demand for Money and Monetary Policy in Egypt: An Empirical Investigation. Mimeo presented to the Egyptian Banking Institute Conference on Inflation Dynamics and Monetary Policy – Cairo 2008. 67. El-Sheikh, S. (2002), Egypt's gross domestic product: quarterly estimates and a money-demand test. L'Égypte contemporaine. Volume 93 #465-466. p. 5-46. 68. Enders, W., (1995), Applied Econometric Time Series, New York, John Wiley and Sons Inc. 69. Engle, R.F. and C.W.J. Granger, (1987), Co-integration and error correction: representation, estimation and testing. Econometrica, Vol. 55, No. 2. (Mar., 1987), pp. 251-276 70. Fernandez, V. (2003), The Credit Channel in an Emerging Economy, Centre for Applied Economics, Department of Industrial Engineering, University of Chile.

125 71. Friedman, M., (1977), The information value of observing monetary policy deliberations, Harvard Universtiy 72. Friedman, M., (1977), Economic stabilization policy: Methods in optimization, North-Holland, Amsterdam. 73. Friedman, M., and A., J., Schwartz, (1963), A Monetary History of the United States, 1867-1960, Princeton University Press. 74. Glenn, R., (1995), Is there a “Credit Channel” for Monetary Policy?, Federal Reserve Bank of St. Louis 75. Granger, C. W. J. (1969), Investigating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica 37 (3): 424–438. 76. Giavazzi, F. and T. Jappelli, (2010), Savings in Egypt: Evidence from House Hold Surveys, background paper to the World Bank’s report on Saving and Growth in Egypt, The World Bank, accessible at:http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK =478060&piPK=64165421&menuPK=64166093&entityID=000158349_20110113 095021 77. Goldfield, S., "The Demand for Money Revisited," Brookings Papers on Economic Activity (1973 : 3), pp. 577-646. 78. Hafer, R.W. and D.W. Jansen (1991), The Demand for Money in the United States: Evidence from Cointegration Test. Journal of Money, Credit and Banking 23(2): 155-168. 79. Handy, H. (1998), Egypt: Beyond Stabilization; Towards a Dynamic Market Economy. International Monetary Fund, Occasional Paper # 163. 80. Handy, H., (1998), Egypt Beyond Stabilization, Toward a Dynamic Market Economy, IMF Occasional Paper, 163. 81. Hansen, B. and Marzouk, G., (1965), Development and Economic Policy in the UAR, North-Holland, Amsterdam. 82. Hendry, D. and N. Ericsson (1990), Modeling The Demand for Narrow Money in the United Kingdom and the United States. Board of Governors of the Federal Reserve System, International Finance Discussion paper # 383. 83. Hendry, D. and N. Ericsson (1990), Modeling The Demand for Narrow Money in the United Kingdom and the United States. Board of Governors of the Federal Reserve System, International Finance Discussion paper # 383. 84. Hicks, J. R. [1935] (1951), A Suggestion for Simplifying the Theory of Money. Economica 2:1-19. Reprinted in Lutz and Mints, eds. 1951. 85. Hoffman, D., and Tahiri, Ch., (1994), Money Demand in Morocco: estimating long- run elasticities for a developing country, Oxford Bulletin of Economics and Statitics, 56,3,. 86. Hossain, A., 2012, Modelling of narrow money demand in Australia: an ARDL cointegration approach, 1970–2009, Journal of Empirical Economics, Volume 42, Issue 3 , pp 767-790. 87. Hussein, K. A., c. (2002), Finance and Growth in Egypt, presented at the conference organized by EPIC and ECES, 24th July 1999, Cairo, www.iceg.org/NE/projects/financial/growth.pdf 88. Hsiao, C., T. W. Appelbe, and C. R. Dineen. "Y. Shen and H. Fujiki, (2005), Aggregate vs Disaggregate Data Analysis—A Paradox in the Estimation of Money

126 Demand Function of Japan Under the Low Interest Rate Policy." Journal of Applied Econometrics 20: 579-601. 89. Holmstrom, B., and J., Tirole, (1997), Financial Intermediation, Loanable Funds and the Real Sector, Quarterly Journal of Economics, No. 112 Vol. 3, pp. 663-91. 90. Ikram, K., (2006), The Egyptian Economy 1952-2000: Performance, Policies and Issues, Routledge, London and New York. 91. Ireland, P., (2005), The Monetary Transmission Mechanism, Mimeo Prepared for The New Palgrave Dictionary of Economics, Second Edition. 92. Johansen, S (1988), Statistical Analysis of Cointegrating Vectors. Journal of Economic Dynamics and Control 12: 231-254. 93. Johansen, S and K. Juselius (1990), Maximum Likelihood Estimation and Inference on Cointegration – with applications to the demand for money. Oxford Bulletin of Economics and Statistics 52: 169-210. 94. Kalman, R. E. (1960), A New Approach to Linear Filtering and Prediction Problems, Transaction of the ASME—Journal of Basic Engineering, pp. 35-45 (March 1960). 95. Kashyap, A.K. and J.C. Stein, (1995), The impact of monetary policy on bank balance sheets, Carnegie-Rochester Conference Series on Public Policy, 42, pp. 151-195. 96. Keynes, J. M. 1936. The General Theory of Employment, Interest, and Money. New York, Harcourt Brace and World. 97. Keating, J., (1992), Structural approaches to vector autoregression, Federal Reserve Bank of St. Louis Review, 74, pp. 37 –57 98. Khier-El-Din, H., (2008), The Egyptian Economy: Current Challenges and Future Prospects, The American University in Cairo Press 99. Kim, S., and N. Roubini, (2000), Exchange Rate Anomalies in the Industrial Countries: A Solution with a Structural VAR Approach, Journal of Monetary Economics, vol.45, pp.561-586. 100. King, R. and R. Levine (1993), Finance and Growth: Schumpeter Might Be Right, Quarterly Journal of Economics 108: 717-38. 101. Knell, M., and H., Stix, 2006, Three Decades of Money Demand Studies: Differences and Similarities, Applied Economics, 38, pp. 805-818. 102. Kiyotaki, N. and J. Moore, (1997), Credit cycles, Journal of Political Economy 105(2), 211-247. 103. Kwaka, P., (2013), Determinants of National Savings: A Short and Long Run Investigation in Ghana, Journal of Business and Management, (5):1. 104. Lee,C., and Chang,A., (2013), Revisiting the demand for money function: evidence fromthe random coefficients approach, Quantitative Finance, 13:9, 1491-1502 105. Lim, G.C. (1993), The Demand for the Components of Broad Money: Error Correction and Generalized Asset Adjustment System. Applied Economics 25(8):995-1004. 106. Loayza, N., López, H., Schmidt-Hebbel, K., and Servén L. (1998), The World Saving Database, Mimeo, The World Bank, Washington, D.C. 107. Mackenzie, G. A. (1979), The Demand for Money in Egypt. Mimeographed, International Monetary Fund.

127 108. Mankiw, N. Gregory, and Lawrence H. Summers. (1986), Money Demand and the Effects of Fiscal Policies. Journal of Money, Credit and Banking 109. Maziad, S., (2008), Monetary frameworks in developing countries : central bank independence and exchange rate arrangements, published doctoral dissertation, University of St. Andrews, UK, http://hdl.handle.net/10023/476 110. Martner, F., Titelman, K., Un analisis de cointegracion de las funciones de demanda de 111. dinero: el caso de Chile, pp 429, El Trimestre Economico, v60, n238, April-June (1993), Mexico. 112. McCallum, B., (2002), Recent developments in monetary policy analysis: the roles of theory and evidence, Federal Reserve Bank of Richmond Economic Quarterly, 88 (Winter) (2002), pp. 67 –96 113. McNown, R and M.S. Wallace (1992), Cointegration Test of a Long-Run Relation between Money Demand and Effective Exchange Rate. Journal of International Money and Finance 11(1): 107-114. 114. Mehra, Y.P. (1991), An Error Correction Model of US M2 Demand. Economic Review May/June 1991: 3-12. 115. Meltzer, A. (2002), Achieving and Sustaining Price Stability. Contribution to the Conference “An Institutional Framework for Monetary Stability, Monetary Stability Foundation, Frankfurt, December 2002. 116. Miller, S.M (1991), Monetary Dynamics: An Application of Cointegration and Error- Correction Modeling. Journal of Money, Credit and Banking 23(2): 139-168. 117. Mishkin F., (1995), Symposium on the Monetary Transmission Mechanism, Journal of Economic Perspectives, Vol. 9. 118. Mishkin F., (2004), Can Inflation Targeting Work in Emerging Market Countries”, NBER working paper, No. 10646. 119. Mishra, P.; P. Montiel, and A. Spilimbergo, (2012), Monetary Transmission in Low-Income Countries: Effectiveness and Policy Implication, IMF Economic Review 60.2 (Jul 2012): 270-302. 120. Modigliani, Franco. (1970), The Life Cycle Hypothesis of Saving and Inter Country Differences in the Saving Ratio, induction, Growth and Trade, Essays in Honor of Sir Roy Harrod. WA. Elits, M.F. Scott, and J.N. Wolfe, eds. Oxford. 121. Mohieldin, M., (1995), Interest Rate, Saving, Investment and Economic Growth under Financial Repression: The Egyptian Example, paper presented at the 2nd conference on International Finance, Philadelphia, USA, 9–11 July 1995. 122. Moosa, I.A. (1992), The Demand for Money in India: A Cointegration Approach. The Indian Economic Journal 40(1): 101-115. 123. Morris, C. & G. Sellon, (1995), Bank Lending and Monetary Policy: Evidence on a Credit Channel, Economic Review, Federal Reserve Bank of Kansas City. 124. Neaime S., (2007), Monetary Policy Transmission and Targeting Mechanisms in the MENA Region”, Paper presented at the Economic Research Forum (ERF) 14th Annual Conference, Cairo, Egypt. 125. Omran, M., (2001), Detecting the Performance Consequences of Privatizing Egyptian State-Owned Enterprises: Does Ownership Structure Really Matter?, 8th Annual Conference of the Economic Research Forum (ERF), Cairo, Egypt.

128 126. Orden, D. and L.A. Fisher (1993), Financial Deregulation and the Dynamics of Money, Prices and Output in New Zealand and Australia. Journal of Money, Credit and Banking 25(2):273-292. 127. Oskooee, M. and A. Kutan and D. Xi, (2013), The impact of economic and monetary uncertainty on the demand for money in emerging economies, Applied Economics, Volume 45, Issue 23. 128. Oskooee, M., S. Hegerty, and H. Wilmeth, (2012), The Saving-Investment Gap and Income Inequality: Evidence From 16 Countries, The Journal of Developing Areas: (46) 2. 129. Papadamou, S. and C., Siriopoulos, 2012, Banks’ lending behavior and monetary policy: evidence from Sweden, Review of Quantitative Finance, Volume 38, Issue 2 , pp 131-148 130. Poole, W., 1970, Optimal choice of monetary instruments in a simple stochastic macro model, Quarterly Journal of Economics, pp. 197 –216 May 131. Perron, P., (1989), The Great Crash, the oil price shock and the unit root hypothesis, Econometrica, 57, pp. 1361 –1401 132. Perron, P., (1997), Further evidence on breaking trend functions in macroeconomic variables, Journal of Econometrics, 80 (1997), pp. 355 –385 133. Pesaran, M.H., Y. Shin, and R.J. Smith (1996),Testing for the existence of a long- run relationship. DAE Working Paper No 9622, Department of Applied Economics, University of Cambridge. 134. Pesaran, M., Y., Shin, and R., J., Smith, Bounds testing approaches to the analysis of level relationships, Journal of Applied Econometrics, 16 (2001), pp. 289 –326 135. Qureshi, H. (2008), Explosive Roots in Level Vector Autoregressive Models. A working paper presented to Midwest Economic Association Conference 2008. 136. Ramey, V. (1993), How Important is the Credit Channel in the Transmission of Monetary Policy?, Carnegie-Rochester Conference Series on Public Policy, 39: 1 – 45. 137. Romer, Paul M., "Growth Based on In-creasing Returns Due to Specialization," American Economic Review, May 1987b, 77:2, 56-62. 138. Rossi, Jose W., The demand for money in Brazil. What happened in the 1980s?, Journal of Development Economics 31 (1989), North Holland, pp364. 139. Schumpeter, J., (1911), The Theory of Economic Development. Cambridge, Mass. Harvard University Press. 140. Scobie, M., (1983), Food Subsidies in Egypt: Their Impact on Foreign Exchange and Trade, International Food Policy Research Institute, Research Report 40. 141. Sims, A., (1980), Macroeconomics and Reality, Econometrica, 48:1-48. 142. Small, David H., and Richard D. Porter, (1989), Understanding the Behavior of M2 and V2. Federal Reserve Bulletin, Board of Governors of the Federal Reserve System, Washington, 143. Solow, M., A Contribution to the Theory of Economic Growth, Quarterly Journal of Economics, February 1956, 70:65-94. 144. Sriram, S.S. (1999), Survey Literature on Demand for Money: Theoretical and Empirical Work, with Special Reference to Error-Correction Models. IMF Working paper WP/99/64.

129 145. Stephanos P. andC. Siriopoulos , (2012), Banks’ lending behavior and monetary policy: evidence from Sweden, Review of Quantitative Finance and Accounting, February 2012, Volume 38, Issue 2, pp 131-148. 146. Tahir, J., (1995), Recent Developments in Demand for Money Issues: Survey of Theory and Evidence with Reference to Arab Countries. Working Paper 9530, Arab Planning Institute. 147. Touny, M., (2008), Determinants of Domestic Savings Performance in Egypt: An Empirical Study, Journal of Commercial Studies and Researches, Faculty of Commerce, Benha University. 148. UllahR., R. Munir, Z., Hussain and F. Sher, (2010), Rate of Interest, Financial Liberalization & Domestic Savings Behavior in Pakistan, International Journal of Economics and Finance Vol. 2, No. 4; 149. Woodford, M., (2010), Financial Intermediation and Macroeconomic Analysis, Journal of Economic Perspectives 24 (4): 21–44. 150. Youssef, S., (1994), Egyptian State Enterprises: A Sector in Transition”, International Journal of Commerce and Management. 151. Youssef, S., (1996), Structural Reform Program of Egyptian State Owned Enterprises, Current Impact and Future Prospects, Journal of Management Development, Vol. 15, No.5. 152. Ziaei, S., (2012), Transmission Mechanisms of Monetary Policy in Saudi Arabia: Evidence From SVAR Analysis, Journal of Modern Accounting and Auditing, Vol. 8, No. 7. 153. Zivot, E., D.W.K. Andrews, (1992), Further evidence on the Great Crash, the oil price shock and the unit root hypothesis, Journal of Business and Economic Statistics, 10 (1992), pp. 251 –270.

130 Chapter II: Investigating the Existence of a Credit Channel in Egypt

Abstract

Healthy financial intermediation should be able to support monetary policy and transmit economic shocks (Bernanke, 2007; and Stein, 1998). Little research was devoted to less developed economies despite their relatively higher contingency to volatility, political instability and difficulty in obtaining reliable data (Fernandez, 2003). The objective of this chapter is to provide answers to three main questions regarding the conduct of monetary policy in Egypt: (i) Does the bank’s credit operate as a transmission channel for monetary policy and, if not, what is the alternative channel that transmits monetary policy in Egypt? (ii) Is the credit channel important to guide monetary authorities and the financial economics profession? (iii) How can Structural Vector Auto Regression

(SVAR) be adopted to model monetary policy strategy for Egypt? My findings confirm the existence of a—statistically significant—credit channel for monetary policy when considering the monetary base as an operating target. The key outcome is that the credit channel, if properly considered, can help authorities propagate monetary policy shocks to the real economy

JEL Classification Numbers: O53, E4, E5

Keywords: Time-Series Models, Monetary Policy, Transmission Mechanisms, Vector Auto Regression

131 1. Introduction

Healthy financial intermediation should be able to support monetary policy and transmit economic shocks (Bernanke, 2007; and Stein, 1998). Despite the vast amount of literature examining the existence of a credit channel, most of it focused on the US and European economies. Little research was devoted to less developed economies, despite their relatively higher contingency to volatility, political instability, and difficulty in obtaining reliable data (Fernandez, 2003).

In order for policy makers to design appropriate policy measures, it is crucial that they have a clear assessment of the effects and timing of such measures on the real economy.

Accordingly, evaluating the monetary policy at a certain point in time and understanding the channels through which it affects the economy are essential (Boivin, Kiley and

Mishkin 2010).

Although economists agree on the significant impact that monetary policy has on the real economy, the mechanism through which this impact takes place is controversial

(Bernanke and Gertler, 1995). A general consensus used to exist among economists and policy makers that interest rates (the conventional “money view”) are the main transmission mechanism—often referred to as the neoclassical channel. A tight monetary policy through a reduction in the reserves provision will lead to an increase in short-term interest rates affecting the cost of capital. This will, in turn, result in a decline in investment funded by bank loans as well as households’ demand on durable goods, negatively affecting output and employment (Black and Rosen, 2007). Within the conventional interest rate transmission mechanism, regardless of the portfolio of assets

132 banks hold, their role in pushing up interest rates is done through the reduction in money supply that mainly consists of their deposit liabilities (Morris and Sellon, 1995).

Nevertheless, recent developments, which the financial markets witnessed over the last three decades and the changes in monetary policy practice, paved the way to the emergence of other channels of monetary policy transmission known as “credit channels,” besides the traditional interest rate channel, often referred to as non- neoclassical channel which considers financial markets as imperfections (Boivin, Kiley &

Mishkin 2010). Market imperfections are due to information asymmetries between borrowers and lenders. Accordingly, financial assets are imperfect substitutes (Ramey

1993). The credit channel is based on the idea that central bank policy affects the supply of loans which, in turn, affects the output. This idea is in contrast with the traditional money view where there is no reference to loan supply shocks. According to the latter, monetary policy affects the decisions to invest and save that predominate when financial markets are complete.

The major shortcoming of the money view is the difficulty to quantify the effect on aggregate spending and investment. The credit channel emerged to fill that gap, and therefore credit and money view channels are considered to be complementary. There are two sub-channels of the credit channel, namely the balance sheet channel and the bank lending channel. According to the balance sheet channel, changes in monetary policy affect the creditworthiness of borrowers due to information asymmetries, and thus banks will cut back on their loans which will lead to the decline of investment expenditures.

133 On the other hand, the bank lending channel is based on the idea that banks have a dual nature as holders of reserve-backed deposits and as loan providers which can affect the availability of bank loans. Commercial banks are considered the main source of finance to investment expenditure and consumption of small firms and households, respectively.

The structure of a banking system is of great importance since it can create the required conditions for a bank lending channel to be effective.

Additionally, inefficiencies and rigidities of financial markets in general and of the banking sector in less developed economies in particular, should be considered when analyzing the effectiveness of monetary transmission mechanisms. A relevant example is the co-existence of an informal market for foreign exchange parallel to the official market. A reduction in the nominal money stock leads to an appreciation in the parallel exchange rate when announced, as well as during the transition period, followed by a subsequent depreciation. This in turn distorts the monetary transmission mechanism

(Agenor, 1995). Moreover, greater tribute should be given to credit market imperfections in less developed economies and sluggish adjustment of bank deposit rates that may impart to a substantial degree of persistence in the response of both output and inflation to monetary shocks (Agenor and Koray, 2009). This is in addition to excess liquidity that may reflect on greater adherence to the deposit rate in response to a monetary contraction, and may translate into a lower risk premium hampering the ability of a contractionary monetary policy to lower inflation (Agenor and Aynaoui, 2008).

Mishra, Montiel, and Spilimbergo (2012) recently assessed the effectiveness of monetary transmission in low-income countries. They define key factors needed for monetary transmission mechanism to efficiently operate. They pre-assume the following

134 institutional setup, which is typically taken for granted in discussions of monetary transmission in OECD countries:

• A strong institutional environment, so that loan contracts are protected and

financial intermediation is conducted through formal financial markets.

• An independent central bank.

• A well-functioning and highly liquid interbank market for reserves.

• A well-functioning and highly liquid secondary market for government securities

with a broad range of maturities.

• Well-functioning and highly liquid markets for equities and real estate.

• A high degree of international capital mobility.

• A floating exchange rate

They concluded that weak institutional framework prevalent in less developed economies drastically reduces the role of securities markets. Consequently, traditional monetary transmission through market interest rates and market-determined asset prices are weak or nonexistent. By the same token, the exchange rate channel tends to be undermined by heavy central bank intervention in the foreign exchange market. The weak institutional framework also has the effect of increasing the cost of bank lending to private firms.

Coupled with imperfect competition in the banking sector, banks are encouraged to maintain chronically high excess reserves and to invest in domestic public bonds or, when possible, in foreign bonds. By a process of elimination, the bank lending channel remains as the most viable general mode for monetary transmission in less developed economies.

135 Therefore, assessing the efficiency of financial intermediation in less developed economies is crucial. This chapter focuses on empirically investigating how imperfections in financial markets in less developed economies—as they apply to

Egypt—reflect on propagation of monetary transmission with an emphasis on the credit channel. Less developed economies rely heavily on bank credit, particularly to the private sector, in order to finance economic development (Cihak, Demirguc-Kunt, Feyen, and

Levine 2013). Accordingly, testing for the efficiency of this channel as a monetary transmission mechanism is a feasible research question worthy of consideration.

This exercise will answer this question with special application to less developed— open—economies that are undergoing substantial transition. There was no, or very little if any, attention paid to less developed—transitioning—open economies in the published

20 literature particularly during the past five years P19F .P These are a group of countries that have undergone social, economic, and political transitions during the past five years of what is

21 called the Arab Spring P20F .P Some of these economies still struggle with political turbulence, including Syria and Libya, while others, including Egypt, Tunisia, and Yemen, prosper with economic recovery. The primary objective of this chapter is to contribute to a deeper understanding of how the recent political and social turmoil affected the monetary and real economies in these countries. Egypt is considered a good candidate for many reasons, including the fact that financial systems are bank based, accordingly banks credit can play a pivotal role to propagate monetary policy shocks to the real sector. This is in addition to higher quality of data relative to other Arab Spring countries, the rich history

20 This statement is based on thorough investigation that I did to the published literature during the past five years in key, high impact factor academic journals, 21 This term is usually used to reference countries that witnessed political regime changes in the Middle East during the past five years including Egypt, Tunisia, Libya, Yemen and Syria.

136 of events during the past two decades, including a regional Gulf War, global crises, and two revolutions. This builds a strong case to pursue this research, which is definitely of interest to the economics profession.

Helpfully, Mishkin (1995) describes the various channels through which monetary policy actions, as summarized by changes in either the nominal money stock or the short term ‐ nominal interest rate, impact real variables such as aggregate output and employment.

What follows are descriptions of the four key channels of transmission of monetary policy and their functionality in the case of Egypt. i. Interest rate channel

This interest rate channel is mentioned in the traditional Keynesian textbook IS-LM model, due to Hicks (1937) originally, and it also appears in the more recent New

Keynesian models. According to the traditional Keynesian interest rate channel, a policy induced increase in the short term nominal interest rate leads primarily to an ‐ ‐ increase in longer term nominal interest rates, as investors act to arbitrage away ‐ differences in risk adjusted expected returns on debt instruments of various maturities, as ‐ described by the expectations hypothesis of the term structure. If the nominal prices are slow to adjust, the increase in the nominal interest rates will translate into an increase in real interest rates, as well leading to the rise in the real cost of borrowing. Therefore, investors will cut back on their investment expenditures. Likewise, households will decrease their demand on durable goods. As a result, aggregate output and employment fall.

137 The interest rate in Egypt couldn’t stand as an indicator for monetary performance.

Interest rate movements barely reflect cyclicality of the economic activity. The Egyptian monetary system possessed multiple rates, including the overnight domestic currency interbank market rate that was introduced in 2001, which was extremely volatile at the beginning, and hardly a good measure of the monetary stance. This is in addition to the three-month treasury bill rate that could be considered, to some extent, a short-term policy rate. ii. Exchange rate channel

In an open economy, a policy induced increase in the short term interest rate will result in ‐ additional real effects through the exchange rate channel. When the domestic nominal interest rate rises above its foreign counterpart, equilibrium in the foreign exchange market requires that the domestic currency gradually depreciate at a rate that, again, serves to equate the risk adjusted returns on various debt instruments denominated in ‐ each of the two currencies—this is the condition of uncovered interest parity. This expected future depreciation requires an initial appreciation of the domestic currency; so that if prices are slow to adjust, domestic goods will be more expensive than foreign goods. Net exports will fall; and domestic output and employment will fall as well.

The exchange rate channel generally constitutes the most important channel of monetary policy transmission in Egypt; it has passed through many phases since 1997, ranging from a pegged regime with intervention in the foreign exchange market, followed by a series of devaluations. In 2001, horizontal bands were set which was considered an unsuccessful policy to face market imbalances. It contributed to creating an active parallel market that offered an attractive rate. In 2003, authorities decided to apply a

138 floating exchange rate policy, which lacked the public’s trust, until the Central Bank of

Egypt (CBE) put in place a dollar interbank mechanism that contributed to the stability of the exchange rate. iii. Asset prices channel

The asset price channel is highlighted by Tobin’s (1969) q theory of investment and

Ando and Modigliani’s (1963) life cycle theory of consumption. Tobin’s q measures the ratio of the stock market value of a firm to the replacement cost of the physical capital owned by the firm. Everything else being equal, a policy induced increase in the short- term nominal interest rate makes debt instruments more attractive than equities; hence, to achieve equilibrium in securities markets, equity prices must fall. Facing a lower value of q, each firm must issue more new shares in order to finance any new investment project; in this sense, investment becomes more costly for the firm. As a result, investment projects, which were only marginally profitable before the monetary tightening, will go unfunded after the fall in q, which result in the fall of output and employment as well.

Meanwhile, Ando and Modigliani life cycle theory of consumption assigns a role to wealth as well as income as key determinants of consumer spending. This theory also identifies a channel of monetary transmission: if stock prices fall after a monetary tightening, household financial wealth declines, leading to a fall in consumption, output, and employment. According to Meltzer (1995), asset price movements beyond those reflected in interest rates alone also play a central role in monetarist descriptions of the transmission mechanism. Indeed, monetarist critiques of the traditional Keynesian model often start by questioning the view that the full thrust of monetary policy actions is completely summarized by movements in the short-term nominal interest rate.

139 Monetarists argue instead that monetary policy actions impact prices simultaneously across a wide variety of markets for financial assets and durable goods, but especially in the markets for equities and real estate, and that those asset price movements are all capable of generating important wealth effects which impact output and employment through spending.

The stock market index in Egypt was almost flat between 1998 and 2003, and the rapid development since then could have contributed to the impact that the monetary policy stance has on real activity and prices. Similarly, there is anecdotal evidence of a strong increase in real estate prices over the last few years across Egypt, leading to higher household wealth. iv. Credit channel

There are two distinct credit channels: the bank lending channel and the balance sheet channel. Kashyap and Stein (1994) trace the origins of thought on the bank lending channel back to Roosa (1951), and also highlight Blinder and Stiglitz’s (1983) resurrection of the loanable funds theory and Bernanke and Blinder’s (1988) extension of the IS LM model as two approaches that account for this additional source of monetary ‐ non-neutrality. On one hand, the bank lending channel is concerned with the effect of monetary policy on the credit supply by banks. According to this channel, banks play a special role in the economy, not just by holding deposits that contribute to the broad monetary aggregates, but also by providing loans. For many banks, particularly small banks, deposits are considered the main source of funds for lending and for many firms, particularly small firms, bank loans are considered the main source of investment finance.

Hence, when monetary policy is tightened, banks are expected to reduce their supply of

140 loans because of a reduction in reserves. As a result firms will cut back on their investment expenditures, which will lead to the decline in output as well as employment.

According to Stein (1998), if banks try to offset the decrease in available funds by increasing non-reservable liabilities, their high cost is expected to yield a net reduction in funds available for lending (Black and Rosen, 2007).

On the other hand, Bernanke and Gertler (1995) described the balance sheet channel, often referred to as the flight to quality, which considers the impact of monetary policy changes on the borrowers net worth and assets. Thus, in the presence of financial market imperfections, banks will reallocate the loans supply from low-quality borrowers, assumed to be small firms, to high quality borrowers, large firms, in response to monetary policy tightening. Therefore, there is a direct effect of changes in monetary policy on the firm’s balance sheet, where an increase in interest rates leads to an increase in the firm’s payments in order to service its floating rate debt. In addition, there is an indirect effect, when the same increase in interest rates leads to the reduction of the capitalized value of the firm’s long lived assets. Accordingly, a policy-induced increase in the short-term interest rate not only results in a reduction in the firm’s investment expenditures through the traditional interest rate channel, but also raises the firm’s cost of capital through the balance sheet channel, thereby exacerbating the initial decline in output and employment.

In Egypt, credit cycles well reflected the business cycles during the 1990s and early

2000s. Since 2000, the banking sector almost halted lending to the private sector. Instead banks built up a sizeable portion of government securities with an attractive yield. The sizeable interest-rate differential, between an average of 91 T-bills rate that peaked to 14

141 percent during 2008 and an average deposit rate of 10 percent, gave a comfortable margin for banks and incentive not to lend (see graphs 20 and 21).

The objective of this chapter is to provide answers to three main questions regarding the conduct of monetary policy in Egypt: (1) Does the banks credit operate as a transmission channel for monetary policy and, if not, what is the alternative channel that transmits monetary policy in Egypt? (2) Is the credit channel important to guide monetary authorities and financial economics profession? (3) Finally, how can SVAR be adopted to model monetary policy strategy for Egypt?

The remainder of the chapter is organized as follows. Section 2 provides a comprehensive literature review on the credit channel for monetary policy with a survey of the recent studies on Egypt. Section 3 outlines the modeling strategy and presents the SVAR model. Section 4 introduces the estimated structural relationships including with the impulse response functions to the structural shocks and the variance decompositions. Finally, section 5 provides concluding remarks.

2. Theoretical Motivation

Bernanke & Blinder (1988) (B&B henceforth) argue that most aggregate demand models treat bank assets and bank liabilities asymmetrically. Empirically, the instability of econometric money demand equations has been accompanied by interest in the nature of the relationship between credit and GNP. This relation depicts the credit channel for monetary policy and inspects how bank financing can influence real GDP. Accordingly, we base our empirical investigation on the theoretical foundations outlined by B&B.

142 B&B present a simple model for an aggregate demand for money and credit (bank loans) and they consider it a variant on the textbook IS-LM model. The LM curve is a portfolio balance condition for two classes of assets—money and bonds. In this context they abandon the implicit assumption that customer-market credit (bank loans) is a perfect substitute for auction-market credit (bonds), and financial markets clear only by price.

Instead, they consider three asset classes in their model (money, bonds, and loans).

Preference between loans and bonds is based on the interest rate and the total demand for loans is a function of the interest rates on loans (ρ), interest rate on bonds (i), and the transactions demand for credit represented by real GNP (y).

This is represented in the following total loan demand equation:

d LP P = L(ρ,i,y)

(1)

Loan supply is a function of: bank assets, bank liabilities and reserves (as a constraint).

The total loan supply equation is represented as follows:

s LP =P λ(ρ,i)D(1- τ)

(2)

The condition for clearing the loan market is:

L(ρ,i,y)= λ(ρ,i)D(1- τ)

(3)

The money market is represented by a conventional LM curve. Suppose banks have excess reserves equal to:

143 2 ε(i) D(1- τ) P

(4)

Then the supply of deposits ignoring cash is equal to bank reserves R, times money multiplier.

-1 m(i)=[ ε(i) D(1- τ)+ τ] P P .

(5)

The demand for deposits arises from the transactions motive and depends on the interest rate, income, and total wealth which is constant and therefore suppressed: D(i,y)

Equating the two:

D(i,y) = m(i)R

(6)

D(i,y) and L(ρ,i,y) define the non-bank public's demand functions for bonds since the money demand +bond demand – loan demand must equal total financial wealth.

The remaining market is the goods market summarized in a conventional IS curve:

y = Y(i, ρ)

(7)

Substituting in (1) by

(1- τ) m(i)R

ρ = Ф(i,y,R)

(8)

Substituting into (3)

y = Y (i, Ф(i,y,R))

144 This is what B&B call the "CC" Curve, standing for "commodities" and "credit".

Accordingly, the CC curve depicts the link between bank credit and real economy. The

CC curve is negatively sloped for the same reasons as IS and is shifted as a result of the monetary policy (R) and by credit market shocks that affect L or λ while IS is not.

B&B found that most conventional shocks work for their model. For example an expenditure shock shifts the CC curve along a fixed LM curve, while a money demand shock shifts the LM curve along a fixed CC curve, which lays the theoretical foundations for the impact of monetary policy shocks through bank credit and money on interest rates and output.

This motivates the research question that this chapter is attempting to answer: whether bank credit to households and private sector propagates monetary policy shocks to real output in Egypt and, accordingly, assesses the effectiveness of credit channel for monetary policy if it exists.

3. Literature Review on the Credit Channel of Monetary Policy i. Recent Developments in Literature on the Credit Channel of Monetary Policy in Developed Economies:

Early literature assessed the effectiveness of monetary policy by studying the relationship between money and output as well as bank loans and output, mainly by using correlations or Granger-causality tests. Most of the literature was in favor of the money view channel.

Although the magnitude of financial market imperfections was recognized by numerous

(9)

145 surveys, the empirical evidence for the existence of a banking lending channel was still ambiguous.

During the 1990s, many studies tried to assess the existence of the banking lending channel. Bernanke and Blinder (1992) used aggregated US data and their results support the banking lending channel. Other SVAR studies that were in favor of the presence of the credit channel are Kashyap et al. (1993) and Gibson (1997). They used data for the

US and found that the composition of bank balance sheets has an impact on the strength of the transmission mechanism. Whereas, Carlino and DeFina (1999), following the theory proposed by Peek and Rosengren (1995), found that the credit channel is not present at the state level and the greater the concentration of small banks, the lesser the response to the monetary policy shifts at the state level. This result contradicts the theory advocated by Kashyap and Stein (1994).

As for the European Union (EU) countries, Ramaswamy and Sloek (1998) and Clements et al. (2001) showed that a credit channel is present in most EU countries. On the contrary, Gerlach and Smets (1995), and Ehrmann and Worms (2001) revealed similar results against the effectiveness of the credit channel in Germany while Hortemoller

(2003) offers a contradictory outcome. Agung (1998) observed that the banking lending channel exists in Indonesia and consumer loans of all bank categories fall because of monetary tightening. On the other hand, empirical findings of Bacchetta and Ballabriga

(2000) and Dedola and Lippi (2000) provide fairly supportive evidence for the balance sheet channel, and inconclusive or partially consistent evidence for the banking lending channel.

146 The literature supporting the existence of a credit channel as a monetary transmission mechanism was centered on two main conditions: the dependence of certain borrowers on bank lending so that changes in bank credit supply instantaneously affect their investment and spending decisions; and that bank lending is constrained by monetary policy decisions

(Morris and Sellon 1995).

In their paper, Bernanke and Gertler (1995) criticized the traditional “money view” transmission mechanism on several aspects. Besides the limitation of the empirical studies in identifying a quantitatively significant effect of the neoclassical cost of capital, monetary policy was assumed to have the highest influence over the short-term interest rates. This assumption contradicts the impact of monetary policy on the purchases of durable assets; and in cases of sensitivity to interest rates, should be more reactive to real long term rates. This gap in the literature led to the emergence of the credit channel transmission mechanism, in an attempt to identify whether the imperfect information and frictions in the financial markets can explain the strength of the monetary policy impact on the real economy.

According to Bernanke and Gertler (1995), the credit channel is not a separate transmission mechanism of monetary policy; rather it strengthens the impact of the conventional interest rate through endogenous changes in the external finance premium.

Changes in interest rates affect the external finance premium in the same direction, amplifying the impact of monetary policy on the cost of capital and, by extension, real spending and economic activity. They explained the mechanism through which monetary policy actions affect the external finance premium by the balance sheet and bank lending channels.

147 Moreover, unlike the conventional monetary transmission mechanism, Bernanke and

Gertler (1995) emphasized the role of the balance sheet channel in explaining the changes in household spending on durable goods, namely housing, in response to monetary policy actions.

Morris and Sellon (1995) provided little evidence on the bank lending channel.

According to them banks’ lending is not restricted by monetary policy actions. Their supporting argument was that banks sell securities and issue managed liabilities in order to overcome the reduction in their deposits and maintain their lending business. In the meantime, little evidence was provided that banks decrease their credit supply in response to contractionary monetary policy. On the contrary, Morris and Sellon (1995) provided evidence supporting the balance sheet channel on the basis that some borrowers, who mainly depend on bank loans, change their level of spending based on changes in bank credit.

A common challenge that faced empirical studies investigating the existence of the credit channel monetary policy transmission mechanism was the difficulty in observing the impact of monetary policy changes on the credit supply and demand separately. The difficulty is a result of the simultaneous effects of monetary policy on both loan supply and demand due to changes in the cost of capital and external finance premiums. Several empirical studies attempted to overcome such a challenge using different proxies for loan supply and loan demand.

Black and Rosen (2007) attempted to empirically identify the contribution of each of the bank lending and balance sheet channels of the credit transmission mechanism. Their

148 main contribution was their attempt to separate the changes in loan supply and loan demand as a result of monetary policy changes. Unlike Kashyap, Stein and Wilcox

(1993), who used the outstanding commercial paper as a baseline for loan demand, considered the stickiness of commitment loan supply relative to spot loan supply in order to differentiate the changes in loan supply from loan demand. In the case of monetary contraction, banks can respond instantly by changing the level of spot loan supply and amending their approval criteria. Using data extracted from the Survey of Terms of

Business Lending (STBL) during the period Q3 1982 to Q1 2006, they applied two empirical specifications to investigate the changes in bank lending as a result of monetary policy changes. The first specification was based on the ratio of commitment loans to spot loans, while the second specification tried to determine the driving forces behind the changes in these ratios. Their empirical results supported the existence of both the bank lending and the balance sheet channels. In response to monetary contraction, banks change the maturity of their loans; i.e. they reallocate their short-term loan supply from small firms to large firms, enhancing the balance sheet channel.

Similar to Black and Rosen (2007), Ciccarelli, Maddaloni, and Peydro (2010), referred to hereafter as CMP, tried to address the challenge concerned with the separation of the changes in credit supply and demand. CMP employed standard VAR models using a new dataset extracted from the US Senior Loan Officer Survey and the Euro area Bank

Lending Survey during the period Q3 1992 to Q4 2009 and Q4 2002 to Q4 2009, respectively. Data on changes in loan demand and lending standards, due to changes in a firms’ net worth and households’ risk, and due to changes in banks’ balance sheet capacity, were collected to empirically assess the impact of monetary policy measures on

149 GDP growth and inflation through the credit channel transmission mechanism. The CMP empirical study provided supporting evidence to the operation of the credit channel as a transmission mechanism. They concluded that the credit channel strengthens the impact of monetary policy changes on output. Additionally, empirical results indicated that for firms, the impact of the credit channel is stronger through credit supply (bank lending channel) relative to balance sheet and loan demand. On the contrary, loan demand and balance sheet channels tend to amplify the credit channel’s transmission mechanism relative to the bank lending channel.

Papadamou and Siriopoulos (2012) used an unrestricted VAR model and impulse response analysis for quarterly data covering 1996 to 2008 to investigate the efficiency of the credit channel in Sweden. Their results showed that a shock on the policy rate affects the main components of the banks’ loan portfolios differently. Initially, banks do not reduce lending to firms and households and they present a sluggish reaction concerning the relevant interest rates. On the contrary, they reduce lending to mortgage credit institutions significantly, since real estate lending can be considered as a risky long-term investment. Hence, reinforcing the evidence of the bank lending channel in Sweden working via mortgage lending could be very important for policy makers.

Ziaei (2012) used quarterly data covering 1992 to 2007 to evaluate different channels of the monetary policy transmission mechanisms in Saudi Arabia. He adopted a baseline of

SVAR models. Contemporaneous coefficient in the structural model indicates that while

Saudi Arabia pegs its currency to the US dollar, a monetary policy instrument reacts positively to unexpected changes in the monetary aggregate. In addition to the traditional interest rate channel, the author also found evidence on the effects of the credit channel.

150 Dave, Dressler, and Zhang (2013) used quarterly data covering 1976 to 2005 for the US, including 108 macro-economic indicators such as measures of industrial production, price indices, employment, and other key macro-economic and financial variables, which contain useful information when identifying the state of the economy, earlier used by other studies (Bernanke, Boivin, and Eliasz, 2005) and (Boivin, Giannoni, and Mihov,

2009). They examined the bank lending channel of monetary transmission in a Factor-

22 augmented Vector Auto-regression (FAVAR)P21F .P They found that the existence of the bank lending channel is more prevalent than previously thought using aggregated lending data, while the lending responses of individual banks are driven more by specific innovations than monetary shocks.

The main takeaways from the review of most recent literature confirm that the bank lending channel for monetary policy matters in developed economies and high income – resource rich countries. However, as mentioned earlier, little quality research was published during the past five years in order to investigate the presence of a credit channel in less developed economies. ii. A Critical Review of the Literature on the Credit Channel of Monetary Policy in Egypt

In general, very little or no work has been done on the credit channel of monetary policy in Egypt. The review of the literature only found three research papers on the feasibility of the inflation targeting mechanism in Egypt, namely; Al-Mashat and Billmeier (2007),

Awad (2010), and Abdel-Baki (2010). The first two studies investigated the effectiveness

22 FAVAR exploits large numbers of macro-economic indicators and allows to consider an alternative identification of monetary shocks and analyze the lending response of banks at the aggregate and individual levels.

151 of the credit channel amongst other monetary transmission channels, yet the third only focused on the interest rate channel for monetary policy.

Al-Mashat and Billmeier (2007) conducted a study using monthly data covering 1996-

2005 that tested the hypothesis that the monetary transmission mechanism exists in

Egypt. They adopted a VAR framework to model various transmission mechanisms, as follows:

Xt =A(L)Xt−1+B(L)Zt +εt

The standard form of the VAR for this specification is seen as follows:

GDP -   GDPt  jtj,     WPI 4 WPI -  t  = +  jtj,  + α 0 ∑β lj,   ε t MPS  = MPS -  t  1j  jtj,  Z    t  Z j-t 

where the endogenous variables (X) were: GDP, Wholesale Price iIndex (WPI),

Monetary Policy Stance (MPS) estimated by Moursi, Mossallamy, and Zakareya (2007)

23 (defined as MMZ 2007 hereafter), P22F P and Nominal Effective Exchange Rate (NEER), while the exogenous variables (Z) are the federal reserve’s fund rate (FFR) and international oil prices. The paper focused on the exchange rate as the most active short run channel used by the CBE to respond to inflationary pressures.

23 MPS: is a measure that MMZ 2007 developed following Bernanke and Mihov 1998 (Just Identified) SVAR model of the market for bank reserves, that identified three equations for (i) total bank’s reserves as a function of innovation in interest rates and residual (vd), (ii) borrowed reserves as a function of innovation in interest rates and residual (vb) and non borrowed reserves that is a function of vb, vd and vs , where vs is the term representing the monetary policy innovations that MMZ estimated as a proxy for Monetary policy stance in a six variables semi structural VAR covering the period 1985 – 2005 for variables including total reserves, non borrowed reserves, borrowed reserves, real GDP, CPI and 3 months deposit rate).

152 After conducting various Granger-causality tests, Al-Mashat and Billmeier concluded

that the interest rate channel provides the correct signs, but the significance and

amplitude of the results were not satisfactory. They also found that the exchange rate

channel continues to play an important role in the transmission of the monetary policy

measures by an MPS measure. Moreover, explicit modeling of the channel was found to

intensify the response of prices to exchange rate shocks. Yet, the effectiveness of this

channel in their view relied on the public sector lending compared to private sector

lending, which is a major weakness that needs further consideration.

However, the most striking finding was that the bank lending channel pointed to a

stronger transmission of the monetary policy stance on output through credit to Public

Enterprises (PEs) compared to lending to the private sector.

On the other hand, Awad (2010) adopted an SVAR approach using As for the exogenous

variables. He included the US’s real GDP, Y Rt,USA R, the general price level measured by

CPI Rt,USA R, and the short-term nominal interest rate measured by the FFR Rt,USA R. As for

endogenous variables, he used Egyptian GDP, WPI, Deposit Rate, M2, and the USD

exchange rate.

Following Christiano et. al (1999), albeit with modifications to adapt for the Egyptian

case to measure the MPS (S RtR); the short-term nominal interest rate (DR RtR), non-borrowed

reserves (NBR RtR), and the ratio of NBRs to total reserves (NBR RtR/TR RtR) where considered:

(SRtR) = [ DR RtR, NBR RtR, NBR RtR/TR RtR ]

As Awad adopted an SVAR approach, he meant to use three main specifications while

altering the sequencing of endogenous variables in order to reflect various assumptions

153 about how the central bank considers his operating targets and feedback between a

variety of variables and the interaction of a monetary policy shock. A monetary policy

shock is identified through a standard Cholesky decomposition with the variables

ordered. The order of S R tR means that it doesn’t have a contemporaneous effect on the preceding variables, yet, it affects the following variables, which are the variations that

were considered for endogenous variables:

(Y RtR) = [y RtR, PRtR, SRtR, M2 RtR, ERtR]

(Y RtR) = [y RtR, PRtR, M2 RtR, SRtR, ERtR]

(Y RtR) = [y RtR, PRtR, ERtR, SRtR, M2 RtR]

Where, Y RtR is a vector of endogenous variables, y Rt R is the real GDP, PRt Ris the measure of the

price level, M2 Rt Ris the nominal money supply, SRt Ris the monetary policy instrument and ERt

Ris the nominal exchange rate. Awad’s (2010) research covered 1995Q1to 2007Q4. He

couldn’t access a reliable source for quarterly data for Egypt’s real GDP so instead he

extrapolated quarterly data for real GDP from annual data using statistical methods

embedded in (E-views 5). He used quarterly imports, exports, the nominal exchange rate,

and M2 as indicator variables to interpolate GDP. The generated GDP quarterly series

was employed together with M2 and the exchange rate in the SVAR regression. This, in

turn, questions the reliability of the findings of this model.

Moreover, various Granger-causality tests concluded that (NBR), and (NBR/TR) were

found to be isolated from foreign exchange changes that the CBE adopts to sterilize the

effect of capital flows.

154 These tests concluded that the short-term nominal interest rate (DR RtR) well represents the monetary policy stance for the Egyptian case, hence, Awad excluded (NBR) and

(NBR/TR) from the analysis for the following reasons:

• Using either NBR REG R or NBR/TR REG R defeated the prior assumption that Egypt is a

small open economy. Block exogenity tests indicated for the case of both

variables, showed that Federal Reserve Bank set its FFR to react to both the rate

of change in real GDP REG R in addition to the rate of change in NBR REG,R when used.

Whereas, when DR Rt,FG R is used as a measure of the monetary policy stance, the

Granger-causality directions were in accordance with our prior description of the

relationship between a small open economy and a large open economy.

• The block exogeneity results, both NBR REG R and NBR/TR REG,R did not reflect the

stance of the CBE on changes in the foreign exchange rate, as neither NBR or

NBR/TR nor the FX rate Granger cause each other. One reason is that the CBE

adopted inconsistent objectives for monetary policy after the introduction of the

Economic Reform and Structural Adjustment Program (ERSAP) at the start of the

1990s (Moursi et al., 2007, Kamar and Bakardzhieva, 2005, and Panizza, 2001).

The CBE has to satisfy the net demand for foreign exchange at the prevailing

foreign exchange rate using sterilized foreign exchange intervention. For instance,

if the CBE is faced with capital outflows that negatively affect the supply of

money, it can maintain its money supply target by purchasing securities with the

same amount of capital outflows (Goodfriend, 2008). In such a case, changes in

either NBR REG R or NBR/TR REG R will be isolated from changes in the foreign

155 exchange rate. As a result, Granger-causality tests will not detect any causality

relationship between these variables.

This clearly defies the need for employing the estimated monetary policy stance measure as adopted by Al-Mashat and Billmeier (2007). Awad’s main findings can be summarized by the fact that CBE (implicitly) maintains a target for the foreign exchange rate. Moreover, it was found that the high variance of domestic interest rates caused by either the FFR or the foreign exchange rate, indicates that the CBE does not apply an independent monetary policy. Foreign economic shocks play a dominant role in explaining the behavior of real domestic growth, whereas domestic economic shocks play a dominant role in explaining the behavior of domestic inflation, especially in the short run. Finally, the interest rate channel appeared to explain the Monetary Transmission

Mechanisms (MTMs) in the Egyptian economy, where changes in domestic interest rate have significant impact on both the rate of growth of real GDP and inflation.

It is also in line with the findings of Abdel-Baki (2010) on the importance of the interest rate in Egypt’s monetary policy framework. Abdel-Baki (2010) tested the efficacy of the

Egyptian monetary agent in cushioning the 2007 to 2010 multi-faceted crises by focusing on the two most significant MTM channels for Egypt, the interest rate and exchange rate channels.

A seven variable SVAR model is employed to estimate the extent of internal and external shocks. This research assumes a dual MTM channel of exchange rate and interest rate. It covered the period 2003to March 2010 to encompass the period of the adoption of inflation targeting in Egypt, in addition to the implicit utilization of the exchange rate as

156 an intermediate monetary target. As per the author, the interbank overnight rate was

chosen as the operational target, which was enacted by introducing the corridor system or

a spread within which bank lending and borrowing rates are allowed to fluctuate. The

corridor band varied from 3 percent to 2.5 percent, indicating slight efficiency gains. This

was blended with a managed float exchange rate regime to maintain an upper hand on

consumer prices as well as accumulating large foreign exchange reserves. Abdel-Baki’s

model examined three types of monetary policies: foreign exchange intervention by

selling and buying foreign currencies; setting the corridor rate; and changing money

supply through the monetary base, following Kim (2001).

The seven variable SVAR was devised such that first three rows of the matrix denote

monetary policy shocks, namely the policy rate or the interbank rate (PR), money supply

24 (M), and foreign exchange intervention (FX) P23F ;P the next two rows display the general price level measured in terms of the consumer price index (CPI) and the level of

aggregate output (O); and the last two rows measure the NEER and the composite food

and oil prices (FO).

Responses of each variable to a one-standard deviation policy rate shock over 12 months

were measured. The outmost lines are within 90percent probability bands or 1.65

standard error bands. In response to contractive policy rate shocks, the deflationary

reaction of the CPI, and oil and food prices were found to be sluggish; hence providing

evidence that policy rate is not that effective in taming soaring prices.

24 While the paper didn’t document the source of this measure, it defined it only as the net effect of the Central Bank’s intervention in the foreign exchange market. It was implicitly understood that the author used the first difference of the stock of foreign exchange reserves. A positive change would mean that the Central Bank was a net buyer, on the other hand a negative change would mean that it was a net seller.

157 On the other hand, the foreign exchange intervention shocks had a broader effect on the exchange rate than the policy rate shock on domestic prices. This concluded that in order to tame prices of imported food items, the CBE intervened through net sales of foreign currencies, drawn from foreign exchange reserves. iii Contribution to the Empirical Literature:

This chapter bridges the gap in the literature on less developed economies and empirically investigates the presence of a credit channel for monetary policy transmission in Egypt. The arguments presented in this chapter build on the rigorous findings of the recent literature, emphasizing active bank lending as a possible channel for monetary transmission for less developed economies with inefficient and relatively more rigid financial systems.

This comes timely as Egyptian monetary authorities are assessing various means to improve the MTMs for monetary policy. A new banking law was passed in 2003 to grant full independence in designing and implementing monetary policy to the CBE. This coincided with the formal adoption of a managed floating exchange rate regime. CBE abandoned setting the exchange rate as a nominal anchor and announced its goal to maintain price stability through the adoption of Inflation Targeting (IT) since 2004.

Earlier studies on the topic in Egypt didn’t capture the response of monetary variables to recent developments in Arab Spring countries, and in particular, Egypt. Accordingly, the contribution of this research to the literature will span:

158 a. Encompassing weaknesses in earlier research

Al-Mashat and Billmeier (2007) concluded that credit to Public Enterprises (P/E) transmits monetary propagations. This finding is challenged since monthly credit data during the period 2001 to 2009 shows that credit to P/E to total credit has dropped by 50 percent. This is further highlighted as the share of credit to P/E to total credit represented almost 14 percent on average during 2001, and was slashed to represent around 7 percent of total credit in 2009. Moreover, the monthly growth of credit to public enterprises is varying around an average of 0.4 percent month to month (almost 5 percent annually) during the period 2000 to 2009, compared to an average monthly growth of 0.7 percent for private sector credit (almost 9 percent annually). The fact is that the Egyptian economy was witnessing annual real growth of 5 percent on average during the period under study. This in turn reinforces my argument for the need to reconsider the findings of this research and reinvestigate rigorously the presence of a credit channel in Egypt.

This is in addition to the need to fine tune the model in order to better interpret the factors influencing the efficiency of the bank credit as a channel for transmission of monetary policy.

In 1990, the Egyptian government owned 399 enterprises with total assets reaching about

$20 billion (14 percent of GDP), and debt of over $40 billion (8 percent of GDP).

Public investments represent 70 percent of total investments in Egypt on average during the early 1990s (Youssef 1994, 1996). The government took steps to privatize the economy in 1991. By the end of 1996, more than 50 percent of government stake in state owned enterprises were sold through transference of ownership to the private sector of

102 companies. In addition, the government liquidated a number of other enterprises,

159 particularly in manufacturing (cars, garments, and metallurgical sectors) due to bankruptcy, and as a result the share of public enterprises to GDP fell to roughly 1 percent of GDP in1996 (Omran, 2001).

The current research will accommodate for issues of data accuracy and seek more

consistent and robust results. Al-Mashat and Billmeier’s (2007) findings rely heavily on

two findings of earlier work by Moursi, Mossallamy, and Zakareya (2007). The latter

employed techniques developed by Litterman (1983) and Chow and Lin (1971) to obtain

high frequency monthly data for GDP to develop their monetary policy stance index. This

approach provides a lot of room for issues of model parsimony and consistency to raise

and question the reliability of the research findings.

b. Ensure better data quality

This research employs a more robust dataset with a longer time horizon that covers many

significant political, social, and economic incidents that were missed by earlier research.

It is crucial to test how monetary aggregates interacted and responded to these incidents.

For example, Al-Mashat and Billmeier (2007) and Awad (2010) employ a dataset that

covers the period 1996 to 2007) and doesn’t account for the 2008 global financial crises,

the Eurozone crises, or the Arab Spring. These incidents provide a rich laboratory for

many research questions. This research uses monthly data covering the period 1997 to

2013 to encompass earlier research and provide more robust findings and policy

recommendations. Moreover, Awad (2010) used indicator variables in the interpolation

of quarterly GDP that were later employed in the SVAR analysis. This research

encompasses this through employing quarterly GDP from credible national sources for

160 this period. This series is then interpolated using monthly imports by adopting Chaw- Lin methodology, which is more robust than other interpolation methods.

4. Hypothesis to Be Tested and Modeling Strategy

This research provides a contribution to the empirical literature on the macro-monetary variables during the Arab Spring. It tests the hypothesis that the credit channel for monetary was active and efficient as part of the monetary transmission mechanism in

Egypt. It tests the null hypothesis that the credit to public enterprises fell so much, relative to total credit, that it became insignificant.

In view of the literature review and following Bernanke and Blinder (1988), a set of six endogenous variables is proposed and included in the following order: (1) log of GDP,

(2) log of CPI, (3) monetary aggregate being real money plus quasi money (M2), (4)

MPS, being the three months deposit rate, (5) a proxy of the bank lending channel; log of

Credit to Private Sector and Households, and (6) log of Real Effective Exchange Rate

(REER). This is in addition to two exogenous variables: (1) the Federal Reserve funds’ rate and (2) a log of international oil prices.

The purpose of employing a log form in this case is the need to determine the sensitivity or elasticity of the output to credit to private sector and households, rather than using level data. This is geared towards broadening the scope of this research to test the null hypothesis that the effect of monetary policy is magnified as a result of change in banks’ lending to the private sector and to households. The intuition is that the credit channel should propagate and magnify the two main policy instruments: banks’ reserves

161 (Bernanke and Mihov, 1998 and Mishkin, 1995) and the interest rate (lending rate to

firms) (Bernanke and Gertler, 1995).

That is, if monetary contraction squeezes banks’ reserves and deposits, the quantity of

available bank loans is reduced. This limits finance opportunities for borrowers and leads

to a fall in output. Therefore, the efficacy of the credit channel partially depends on the

ability of the central bank to affect the supply of loans by altering reserve requirements

(Mishkin, 1995).

However, such analysis should also consider the case when the monetary authority

increases the interest rate for bills, followed by a reduction of bank reserves (and

deposits) which in turn culminates in an additional influence on the availability of new bank loans (Bernanke and Gertler, 1995). The aim is to test this empirically through a

VAR model.

25 The SVAR analysis P24F P will be adopted to provide a vehicle for the empirical “validation”

of various transmission channels identified in the theoretical literature. This research will

follow Keating (1992) which provides a comprehensive survey of the literature on VAR

models. A significant conclusion of his research shows that the VAR model structure and

covariance matrix of the regression residuals make this model a reduced form, where

instantaneous correlations among endogenous variables are left uninterrupted.

25 There is a vast array of literature utilizing SVAR models to analyze the macroeconomic effects of monetary policy. The relative merits of the SVAR approach have been widely discussed (see Sims, 1986; Bernanke, 1986; Blanchard and Watson, 1986; Shapiro and Watson, 1988; and Blanchard and Quah, 1989) and it is beyond the scope of this study to review this argumentation.

162 However, adoption of SVAR models allows for the contemporaneous relationships

among endogenous variables. It also allows for orthonormal random shocks to hit the

system based on certain theoretical priors, and provides more meaningful results that can better guide policy, particularly on transmission channels for monetary policy (see Sim,

Blanchard, and Quah, 1989; Kim and Roubini, 2000; and Cheng, 2006 and others).

The proposed SVAR structural form is:

= + + +e yt A1yt- 1. .. A y - ptp t

Where,

y t is a 6 vector of endogenous variables,

yt = [(RGDP RtR), (CPI RtR), (Monetary Policy Stance RtR), (M2 RtR),

(Credit to Private Sector and Households RtR), (REER RtR)]

A,...A andB 1 p are matrices of coefficients to be estimated e t is a vector of innovations that may be contemporaneously correlated but are uncorrelated with their own lagged values and uncorrelated with all of the right-hand side variables.

y ∑= ′]ee[E Let t be a k-element vector of the endogenous variables and let tt be the

residual covariance matrix. Following Amisano and Giannini (1997), the class of SVAR

models can be written as:

= e Aet B t

163 e e e Where, t and t - are vectors of length, k. t is the observed (or reduced form) residuals,

u while t is unobserved structural innovations. A and B are k*k matrices to be estimated.

u The structural innovations t are assumed to be orthonormal, i.e. its covariance matrix is

E[e e ' ] = I e an identity matrix tt . The assumption of orthonormal innovations t imposes the following identifying restrictions on A and B:

A∑A′= BB ′

A∑A′= BB ′ k(k - 2/)1 2 Noting that are symmetric, this imposes restrictions on the 2k P P

unknown elements in A and B. Therefore, in order to identify A and B, there is a need to

2 - + = impose at least 2k k(k 2/)1 k(k 2/)1- additional restrictions.

The baseline model includes the same endogenous and exogenous variables as the reduced

form VAR presented in the previous section. No contemporaneous interaction is assumed

among endogenous variables, and therefore matrix A is just a unit matrix, which is equivalent

to k2 restrictions. This leaves us with the need to impose at least k(k-1)/2 restrictions

equivalent on matrix B. This is quantified as 6*(6-1)/2 = 15 additional restrictions on matrix

B to identify the SVAR.

In the remainder of this section, two different identification schemes, which were widely

used for studies on monetary policy transmission mechanism, will be discussed.

164 A. Identification Scheme One: Recursive VAR

The first identification scheme is the standard approach that imposes a recursive structure

of the VAR, with a particular ordering of variables. A reduced-form VAR will be

considered with six endogenous variables and three exogenous variables all in logarithms

and a constant. An unrestricted VAR will be estimated with an optimal lag length after

conducting the optimal lag length recursive test using Oxmetrics 6.1.

Endogenous variables will be used according to following order:

[(RGDP R tR)→ (CPI RtR) → (MPS RtR) → (M2 R tR) → (Private Credit RtR) → (Real Effective Exchange

Rate RtR)]

All variables are in (log) levels. The lag length was determined based on the Schwartz

information criteria. The inverse roots of the auto-regressive polynomial are within the

unit circle, indicating that the model is stable.

Unrestricted VAR only estimates a transmission, which is given by the Cholesky

decomposition (matrix) and cannot estimate different transmissions, which were assumed by researchers. For these purposes, an SVAR is more suitable. The main purpose of the

SVAR estimation is to obtain non-recursive orthogonalization of the error terms for

impulse response analysis. This alternative to the recursive Cholesky orthogonalization

requires imposing enough restrictions to identify the orthogonal (structural) components

of the error terms.

Intuitively, it assumes that prices (CPI) have no immediate effect on output (GDP),

monetary policy stance (S) has no immediate effect on prices, money stock (M) has no

165 immediate effect on the credit to private sector, and the REER has no immediate effect on the credit to private sector. Technically, this amounts to estimating the reduced form

VAR, then computing the Cholesky factorization of the reduced form VAR covariance matrix.

The relation between the reduced form errors and the structural disturbance is further explained as follows:

= e A( L ) y t t

* = A( Ly ) t e t p A*( L ) = A * ∑ i i=0 *= - 1 A0 P *= - 1 Ai P A i

y Where, t is the vector of endogenous variables, A is the matrix of coefficients, L is a

e e lag operator, t is the vector of the observed error terms from the reduced-form VAR, t

- is the vector of structural innovations, and P 1 is the Cholesky decomposition of the covariance matrix of residuals. The Cholesky decomposition of the covariance matrix of the residuals:

A Matrix B Matrix

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 ∗ = ∗ 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1

166 The Cholesky decomposition instantaneous relation amongst structural shocks can be depicted as:

Table (a):

Real Average Private Effective GDP CPI Lending M2 Credit Exchange Rate Rate RGDP 1 0 0 0 0 0 CPI X 1 0 0 0 0 Monetary Policy X X 1 0 0 0 Stance M2 X X X 1 0 0 Private Credit X X X X 1 0 Real Effective X X X X X 1 Exchange Rate This research covers the two dimensions for conducting monetary policy. One pertains to actions taken by the central bank to adjust the instruments of monetary policy that affect the real economy. Accordingly, the monetary base is considered a useful conduit to measure the monetary policy stance. The other is related to adjusting the instruments of monetary policy—usually one or more key short-term interest rates—in response to changes in variables related to their objectives—the reaction function. Consequently, we follow an empirical strategy for identifying the policy-induced component of changes in output. A starting point for doing this is to assess how the monetary authority’s decisions of changes in the monetary base would be transmitted through the credit channel to the real output. Changes in money or commercial banks' reserves typically reflect demand shocks. The second would focus on short-term interest rates rather than on money or reserves for identifying monetary policy innovations. Most central banks smooth overnight or other short-term interest rates, changing them only when they deliberately intend to change the stance of monetary policy and subsequently account for policy induced shocks.

167 Therefore, two difference variables will be employed alternatively to represent the

monetary policy stance, namely the monetary base and interest rate. This takes into

consideration two classes of models: (1) monetary base (henceforth B models) and (2)

interest rate (henceforth R models). Results for both classes of models were reported.

B. Identification Scheme Two: Non-Recursive SVAR

Unlike the recursive identification, which assumes no contemporaneity between

monetary policy, money, and the exchange rate, following Sims and Zha (1998) and Kim

and Roubini (2000), an alternative identification scheme relaxes these assumptions.

Considering the B model formulation, the following restrictions were imposed to

achieve a just identified model:

A Matrix B Matrix

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 ∗ = ∗ 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 By definition, restrictions are imposed based on assumptions that are in line with the

institutional features of the Egyptian economy. This also provides a venue to assess the

validity of the findings of earlier research on Egypt on the relationship between variables

and whether such relations still hold for the proposed model. The following matrix provides the strategy for imposing restrictions on the B matrix above to identify structural

shocks based on theory for the B model:

168 Table (b):

Average Real Effective Underlying Private GDP CPI Lending M2 Exchange theory for Credit Rate Rate restrictions RGDP 1 0 0 0 0 0 CPI -X 1 0 0 0 0 Phillips curve Monetary Base Monetary Base -X X 1 X 0 0 Reaction Function M2 -X X 0 1 0 0 Money demand Private Credit X X X X 1 X Credit supply Real Effective X X 0 X 0 1 Exchange Rate

• The first equation represents a sluggish response of real GDP with regard to

shocks to the nominal variables, money stock, short-term interest rates, and the

nominal effective exchange rate, which is common in the less developed

economies.

• The second equation represents the simple Phillips curve that models the response

of prices with respect to shocks to output.

• The third equation is a money demand equation, with money demand allowed to

respond contemporaneously to shocks in prices and short-term interest rates.

• The fourth equation can be interpreted as the monetary policy reaction function,

which responds contemporaneously to prices and the exchange rate, but does not

respond immediately to contemporaneous output and money because relevant

data on output and money is usually only available with a lag. Since the monetary

policy stance is represented by interest rates, then this equation is a clear

representation of the fisher equation (see Fisher 1930).

• The fifth equation is the credit supply equation which proposes that credit

responds contemporaneously to all variables in the model.

169 • The sixth equation suggests that the real effective exchange rate responds

immediately to all other variables except CPI.

Consideration of the R model formulation is reflected in the following set of theory

based constraints:

A Matrix B Matrix

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 ∗ = ∗ 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1

This translated to the following strategy for imposing restrictions on the B matrix above

to identify structural shocks based on theory for the R model:

Table (c):

Average Real Effective Underlying Private GDP CPI Lending M2 Exchange theory for Credit Rate Rate restrictions RGDP 1 0 0 0 0 0 CPI X 1 0 0 0 0 Phillips curve Average Fisher Lending 0 X 1 X 0 X equation Rate Money M2 X X 0 1 0 0 demand Private X X X X 1 X Credit supply Credit Real Effective X X 0 X 0 1 Exchange Rate

170 The earlier interpretation for individual equations still holds except for the fourth equation, under the R models. Since the monetary policy stance is represented by interest rates, then this equation is a clear representation of the fisher equation.

The aforementioned strategy is repeated with an alternative formulation with exogenous variables that reflect the economic activity with the US as the largest trading partner and foreign investor in Egypt.

The following diagram summarizes the modeling strategy:

Figure (1)

Identification Monetary Policy Stance (MPS) Strategy

Monetary Base (B) Lending Rate (R)

Recursive VAR Identification Strategy (Choleski Model B (1) Model R (3) Decomposition)

Non Recursive VAR Identification Strategy Model B (2) Model R (4) (Theory- Based Restrictions)

171 5. Data and Variables Selection

The data series was obtained from the International Monetary Fund’s (IMF) International

Financial Statistics (IFS) database accessed online in August 2015.

High frequency monthly data covering the period June 1997 to June 2013 (192 observations) were obtained for CPI, average lending rate, monetary base, broad money

(M2), total private credit (sum of private sector firm and households) and the real effective exchange rate. Annex I details data source and relevant definitions. The GDP was obtained from the IMF – IFS database in annual data and disaggregated into monthly data using Matlab 7.10.0.499. The temporal disaggregation was pursued employing the

Chow-Lin procedure. This method uses a related series as an indicator to convert low- frequency data into high-frequency data based on Chow and Lin (1971). Egyptian monthly imports series was used as an indicator variable.

This approach, developed a static multivariate regression based procedure method, identifies a linear relationship between quarterly observations of a variable and a related monthly series. Chen 2007 emphasizes that this approach argues that the sub-annual series to be estimated could be related to multiple series, and the relationship between the sub-annual series and the observed related sub-annual series is:

xt = z tβ + u t where: x is a Tx1 vector z is a Txp matrix of p related series,

β is px1 vector of coefficients,

172 ut is a Tx1 vector of random variables variables with mean zero and TxT covariance matrix V.

This method assumes that there is no serial correlation in the residuals of the sub-annual

estimates. The following represent the between annual and sub-annual series:

y = B’x = B’z β + B’u

Chow-Lin derives the solutions by means of the minimum variance linear unbiased

estimator approach. The estimated coefficient β is the GLS estimator with y being the

dependent variable and annual sums of the related sub-annual series as the independent

variables. The estimated coefficients are:

-1 -1 = [z’B(B’VB) -1B’z] P z’B(B’VB)P P y,P and the linear unbiased estimator of x is:

-1 =z + VB(B’VB) P [yP – B’z ]. The first term in refers to the observed related sub-annual series of the explanatory variables. The second term is an estimate of the Tx1 vector u of residuals obtained by

distributing the annual residuals y – B’z with the TxM matrix VB(B’VB)-1. This implies that if the sub-annual residuals are serially uncorrelated, each with variance σ2,

2 then V = σ P PITxT, and then annual discrepancies are distributed with A = I TxT.

Egypt’s Nominal GDP series denominated in domestic currency—measured at market prices—in annual frequency is disaggregated into higher monthly frequency using monthly

173 26 imports (in domestic currency) as an indicator function using Chow-Lin method. P25F P This is

also in line with an earlier study by the Egyptian Cabinet Decision Support Center that

identified the Chow-Lin method to be superior to other methods when used to disaggregate

Egypt’s national accounts data (Ismail and Abdelmegeed 2007).

6. Empirical Results

The Augmented Dickey-Fuller (ADF) test results,- as represented in table (14a and 14b),

suggest that the null hypothesis that the variables are I(1) cannot be rejected for levels.

However, the first differences of the data series are all stationary (table 14a). This

reinforces the argument that data is integrated with the first order. As in most VAR

models of the monetary transmission mechanism, we do not perform an explicit analysis

of the economy’s long-run behavior since the primary interest is to understand dynamic properties and interactions in the short term.

By conducting the analysis in levels, we allow for implicit co-integrating relationships in the data. Imposing co-integrating restrictions on a VAR in levels could increase efficiency in the estimation, but given the short data series, it may result in inconsistencies. Since the monetary transmission mechanism is a short-run phenomenon, most comparable studies employ unrestricted VARs in levels to evaluate impulse responses over the short to medium term (Favero, 2001).

Co-integrating restrictions and first differencing the data are both forms of linear subtractive restrictions, which when falsely imposed lead to problems of specification

26 Details of the matlab code and output for the disaggregation process are available upon request. Matlab 7.10.0.499 was used for disaggregation.

174 bias, including potential sign reversals. For a discussion of the dangers of subtractive

restrictions, see Haynes and Stone (1981); Hamilton (1994) has described three

approaches embraced by the literature.

Assuming the presence of variables which are non-stationary and co-integrated, the first

approach is to estimate the model in levels and allow the data to impose their own

restrictions. This method results in consistent coefficient estimates, but some losses in

efficiency may occur because appropriate coefficient constraints (due to co-integration)

are not imposed. A second approach is to construct an error-correction model where the

co-integrating relationships are estimated from the data. The third approach also

constructs an error-correction model, but in this case, the co-integrating relationships are

derived from theoretical considerations rather than from estimation (e.g., Cochrane

(1994)). The possibility, however, of imposing false restrictions, thereby leading to bias

in the coefficient estimates and consequently the dynamics of the model, often leaves

economists wary of the two latter approaches and favoring the unrestricted levels model1

(see, for example, Bernanke and Blinder (1992)).

Should the SVAR model be specified at levels or first difference? It seems the choice is between selecting or accepting a loss of efficiency (when a VAR model is estimated in

levels) and a loss of information (when a VAR model is estimated at first difference).

Ramaswamy and Sloek (1998) provided a detailed discussion on this topic. With the possible exception of inflation, all of the variables used in the study are potentially non-

stationary due to the presence of either deterministic or stochastic trends.

175 This raises the question as to whether the SVAR model should be specified in first

differences rather than in levels. Ramaswamy and Sloek (1998) discuss the trade-off between the loss of efficiency (when the VAR is estimated in levels, but without

imposing any co-integrating relationships) and the loss of information (when the VAR is

estimated in first differences). In essence, they recommend that in cases where there is no prior economic theory that can suggest either the number of long-run relationships or

how they should be interpreted, it is reasonable not to impose false co-integration

restrictions on the VAR model to avoid the misspecification discussed above. Their

recommendation is observed in this chapter; moving forward the VAR and SVAR models

are specified in levels.

i. Monetary Base as a Measure of Monetary Policy Stance (Model B)

The optimal lag length was identified to read 13 lags, down from originally 17.

Sequential reductions of lags from 17 to 13 were achieved whilst testing for statistical

significance of each lag as well as statistical significance of all lags (see table 15a). The

Hannan-Quin (HQ) and SC information criterion confirmed the validity of this selection

as it was minimized at lag 13 whereas Akaike's (1973) Information Criterion (AIC) voted

for 17 lags. What is more, the overall significance for model reduction couldn’t reject the

null of validity of restricting the model at 13 lags. Accordingly, a research decision was

made to select 13 lags.

On another front, the misspecification tests for the reduced VAR model indicated that

individual equations as well as system’s residuals are free from serial correlation (table

15b). However, the model suffers from non normality. This is expected and is common

176 practice when employing monthly data; given the large number of observations (180 observations) and the presence of a number of outliers.

Visual inspection of the individual series, actual residuals, and distribution confirm that residuals are stationary, and that their distribution comfortably fits to the inverted normal distribution curve (graphs 22 and 23). Accordingly, the analysis proceeds to assessing stability of the VAR model.

The set of stability tests confirm that the reduced VAR is stable with all roots contained within the unity circle and results of one step residuals, one step Chow test and break points, Chow stability tests, as well as modulus of the Eigen values of a long run matrix below unity (read 0.8795), all indicating that the model is stable and relevant for inference.

However, model stability is achieved only after introducing three dummy variables: (1) dumm200301 for January 2003, when the decision of floating the exchange rate was decided; (2) dumm201012 for December 2010 to pick up the response of macro variables to political and economic unrest coupling with the first rising waves of revolution on

January 11, 2011, and (3) dumm201301 for January 2013, when the central bank intervened to restore the exchange rate from LE 9 per USD down to around LE 7.30 per

USD from selling USD through banks in the domestic market.

Having estimated the VAR model, the analysis proceeds to modeling the structural relationships based on the Maximum Likelihood method. The structural relationship for the B model was estimated adopting the recursive identification strategy (table 16) as well as the theory based identification strategy (table 17).

177 In general most of the signs of estimated coefficients are in line with theory in both models. The following is brief interpretation of the estimated models:

• In general, the recursive specification seems to provide more robust estimates

than the theory based identification specification, as it indicates more statistically

significant estimated coefficients for the structural equations.

• The positive sign in the aggregate supply function is in line with the theory that

the short run Phillips curve holds for the Egyptian economy. Aggregate demand

innovations determine the course of inflation in the short term. Yet the coefficient

is statistically insignificant in both specifications.

• The monetary base reaction function in both specifications indicates a minimal to

statistically significant response to output. This aligns with the fact that the

monetary base of an operating target is affected by open market operations and/or

the discount rate. Therefore, since it is not an intermediate target, it does not

directly link to economic activity, but rather affects the economy through its

effects on the money supply. On the other hand, the positive and significant

relationship between the monetary base and inflation in both specifications

confirm the fact that the monetary authority failed to adopt an efficient inflation

targeting mechanism.

• The money demand function shows money income elasticity close to zero,

indicating that level of real output does not exhibit any influence over the money

demand.

Most importantly, the positive and statistically insignificant coefficient for the

monetary base in the money demand equation points to the fact that the unit

178 increase in the monetary base as an operational target positively influences M2—

the intermediate target—that grows at 0.2 percent. The credit supply function

shows statistically significant variables that assert the economic theory. The credit

supply is positively affected by the monetary base. It is notable that the positive

identification specifications) affirms the existence of the credit view of the

monetary transmission mechanism for aggregate household and private sector

credit. In other words, the respective positive coefficients point to the fact that the

credit view is statistically relevant when it comes to total private credit, implying

an operative role of the credit view for total private credit. Finally, the credit

supply function points to a negative impact of money demand shocks on the credit

supply function. Higher demand for broad money balances will lead to lower

credit to the private sector. It is worth noting that the coefficient for the monetary

base is equal in magnitude and opposite in signs to the money demand coefficient

in the credit supply function. The real effective exchange rate positively

influences total private credit. This is an indication that bank finances private

sector experienced a loss of efficiency due to appreciation of the real effective

exchange rate due to the rise of foreign price of goods relative to the domestic

price (i.e. a fall in the purchasing power of the domestic currency in relative

terms).

Having examined the B models, it is reasonable to assume that the credit view is statistically supported when the monetary base is taken into account. This was achieved

179 when both the recursive and the theory based identification strategy for Structural VAR modeling were adopted. ii. Lending Rate as a Measure of the Monetary Policy Stance (Model R)

The optimal lag length was identified as 13 lags down from originally 17. Sequential reductions of lags from 17 to 13 were achieved whilst testing for statistical significance of each lag as well as statistical significance of all lags (see table 21). The HQ and SIC information criterion confirmed the validity of this selection as it was minimized at lag

13, whereas AIC voted for 17 lags (table 18). Moreover, the overall significance for model reduction couldn’t reject the null of validity of restricting the model at 13 lags and, therefore, a research decision was made to select 13 lags.

On another front, the misspecification tests for the reduced VAR model indicated that individual equations are free from serial correlation at all significance levels, whereas the system’s residuals are free from serial correlation at a 10 percent significance level (table

22).

However, the model suffers from non-normality. This is expected and common practice when employing monthly date; given the large number of observations (180 observations) and the presence of a number of outliers.

Visual inspection of an individual series, actual residuals, and distribution confirm the fact that residuals are stationary and their distribution comfortably fits to the inverted normal distribution curve (graphs 26 and 27). Accordingly, the analysis proceeds to assessing stability of the VAR model.

180 The set of stability tests confirms that the reduced VAR is stable with all roots contained

within the unity circle and results of one step residuals, one step Chow test and break points, Chow stability tests, as well as the modulus of the Eigen values of long run matrix below unity (read 0.4199); all indicating that the model is stable and relevant for

inference.

However, model stability is achieved only after introducing six dummy variables: (1)

dumm2010004 and (2) dumm201005 for April and May 2010, when the decision of

floating the exchange rate was decided; (3) dumm201007 for July 2010, to pick up the

response of macro variables to political and economic unrest coupling with the first wave

of the rising revolution on January 11, 2011, (4) dumm201011 (5) dumm201101 for

February 2011, when the central bank intervened to restore the exchange rate from LE 9 per USD down to around LE 7.30 per USD through selling USD through banks in the

domestic market, and (6) dumm201112 for November 2011.

Having estimated the VAR model, the analysis proceeds to modeling the structural

relationships based on the Maximum Likelihood method. The structural relationship for

the R model was estimated by adopting the recursive identification strategy (table 23) as

well as the theory based identification strategy (table 24).

In general, most of the signs of estimated coefficients are in line with theory in both

models. The following is a brief interpretation of the estimated models:

• The recursive identification formulation showed more robust results in

comparison to the theory based formulation similar to the findings of the B model.

181 • Under both identification approaches for R model, the short run Phillips curve

holds for the Egyptian economy. The aggregate demand innovations determine

the course of inflation in the short term, and the coefficient is statistically

significant in both specifications.

• The interest rate function that reflects the monetary policy variable in the R class

of models indicates that, nonetheless, the interest rate responds negatively

insignificantly to price level in R1, R2, and R3 models, which provides an

indication of the possible presence of inflation targeting by the monetary authority

following the corresponding interest rate shock. In the same function, shocks in

money demand lead to negative and insignificant effects on the interest rate in R

models.

• Money demand function doesn’t indicate the expected positive relationship

between money demand and output in the R model. However, results validate the

expected positive relationship between money demand and price level. This is in

line with theory that postulates that nominal money demand is proportional to the

price level in the economy. The expected reverse relationship between money

demand and interest rates was also validated in the results.

• The credit supply function fails to validate the economic theory that asserts the

positive relationship between credit and price level in R models. Money demand

shocks affect credit supply negatively and are statistically significant. Credit

supply responds negatively, yet insignificantly, to interest rate innovations which

implies that the banking sector in Egypt does not induce bank loan supply in the

case of interest rate increase brought about by tighter monetary policy. This in

182 turn votes against a credit view of the mechanism for monetary policy

transmission. It is worth noting that credit supply positively responds to the

appreciation of the real effective exchange rate and is statistically significant. The

earlier analysis in the B model section still holds.

• Finally, the real effective exchange rate responds positively to a nominal money

demand shock and a positive credit supply shock, thereby reflecting a drop in

competitiveness as a result of both shocks.

To conclude, our findings confirm the existence of a statistically significant credit channel for monetary policy when considering the monetary base as an operating target.

Total private credit varies positively with the monetary base which indicates an operative credit channel from this perspective. This reflects the monetary authority’s decision to move from a quantitative operational target (excess reserves) to a price target (overnight inter-bank rate), launching a corridor system in June 2005, and issuing central bank instruments for the first time in August 2005 as the primary instruments for liquidity management through open market operations that influence the monetary base.

On the other hand, the date doesn’t vote for the adoption of the lending rate as an operational target. The negative, although insignificant, coefficient of total private credit in response to the lending rate doesn’t support the credit view for a transmission channel upon considering the lending rate as an operating target.

183 7. Granger Causality i. Monetary Base as a Measure of Monetary Policy Stance (Model B)

• Lagged price levels Granger cause output at a 5 percent significance level

indicates that inflation can predict the output behavior in the short term. The

results of the price equation also indicate that this is a unidirectional causality, as

lagged output doesn’t influence future price levels.

• While the lags of other variables in the system individually do not predict the future

output, taken jointly however, the lagged variables within the whole system can

predict future real output and causality runs from the model to real output at a 1

percent significance level.

• Total private credit is the only variable in the system, lagged values of which can

predict inflation in the short term at 5 percent. This causality relationship is bi-

directional as inflation lagged values have predictive power for total private

credit’s future values. Causality also runs from the whole model to inflation at 1

percent.

• The causality between the monetary base and inflation is bi-directional. Monetary

base Granger causes price levels at a 5 percent significance level; whereas price

levels Granger causes the monetary base at the same significance level. The

whole model Granger causes the monetary base at a 1 percent significance level.

• The feedback between the monetary base and total private credit is unidirectional

and flows from the total private credit to the monetary base at barely a 10 percent

significance level.

184 • It is worth noting that output, money demand, and real effective exchange rate

also Granger cause the monetary base, all at a 5 percent significance level. The

causality also runs from the whole model to the monetary base at a 1 percent

significance level.

• Granger causality also runs from output, price level, and monetary base to the

money demand at a 5 percent significance level. This is in line with theory.

• Test results also indicate that inflation, money demand, and real effective

exchange rate all Granger cause total private credit all at 5 percent and the whole

model Granger causes total private credit at a 1 percent significance level.

• Finally, the price level and money demand Granger causes real effective exchange

rate at 5 percent and 10 percent, respectively, and the whole model Granger

causes REER at 1 percent.

We can conclude from the Granger causality tests for the B model that the model is able to Granger cause all variables at a 1 percent significance level, thereby indicating the strong predictive power for the model over variables of interest in the short run.

The operation target (monetary base) can be explained by information from output, price levels, demand for money, REER, and the intermediate target (total private credit), yet at a marginal significance level for the later. It maintains bi-directional causality with the demand for money and inflation, indicating the presence of a feedback effect.

The intermediate target is also interpreted by inflation, money demand, and REER, while maintaining bi-directional causality and feedback with inflation only.

185 Findings from the Granger causality test provide rigorous statistical support to model specification, the ordering of variables in the specification that adopted recursive- identification of structural shocks, as well as the specification theory based restrictions for identification of structural shocks for the B model. ii. Lending Rate as a Measure of Monetary Policy Stance (Model R)

• In general, the model has predictive power over all variables with the exception of

the lending rate. Causality flows from the model to all variables of interest at a 5

percent significance level, with the exception of the lending rate and the REER,

where it is insignificant.

• The only variable that Granger causes output is money demand only at a 10

percent significance level. This relationship is bi-directional as income also

Granger causes money demand at a 5 percent significance level. This indicates the

presence of feedback between both variables.

• Price level also maintains a statistically strong bi-directional causality with total

private credit at a 1 percent significance level, whereas no other variable of

interest Granger causes inflation in this outset.

• The lending rate is neither predicted by lags of other variables of interest in the

model nor the model itself Granger causes this variable at any significance level.

• Output, price levels, and lending rate Granger cause money demand at a 5 percent

significance level. This indicates a unidirectional causality from lending rate to

money demand.

• While lagged output marginally predicts total private credit at a 10 percent

significance level, price level, money demand, and REER Granger causes total

186 private credit at a 5 percent significance level. The causality between total private

credit and REER is uni-directional and flows from the latter to the former.

• Finally, the REER is only predicted by lagged money demand at a 5 percent

significance level. This is a unidirectional relationship with no feedback.

One conclusion from the block exogeneity test for R models is that variables of interest in

the R model maintain little feedback amongst themselves. The lending rate and REER

aren’t predicted by lagged information provided by the model. Nor do lags of variables of

interest predict the lending rate. Lags of REER influence the model only through providing predictive power to the total private credit while lags of the latter don’t provide predictive power to the REER.

Following the model dynamics, total private credit lags maintain a bi-directional causality

with price level. In this outset, money demand plays a key role since it is predicted by

lagged price levels and the lending rate, both in a unilateral causality, whereas it

maintains a bi-directional causality with output. Accordingly, there is an indirect role for

the operating target in this case (the lending rate) to influence the credit channel for the

transmission of monetary policy shocks. The lending rate influences the price level by

influencing money demand that, in turn, influences total private credit. Then the latter, in

turn, influence price levels. Moreover, the lending rate can influence output only

indirectly through money demand.

8. Structural Impulse Response Functions

In this section, the analysis moves forward to study the behavior of the macro-dynamics

of the models. The impulse response function deepens the analysis to assess the outcomes

187 of the estimated structural specification. This involves analyzing the dynamic impact of a

one-time shock to one of the innovations on the dynamic behavior of the system’s

endogenous variables in the current time period as well as in the future. In the analysis,

and given the scope of this research, we only focus on the interaction of variables that are

relevant to the credit channel with all other endogenous variables included in the model.

The impulse response functions are estimated based on defining the impulse as one

standard deviation of the residuals. It is plotted for a time period of ten months for all

variables. The vertical axes depict changes in the variables from their baseline. The

dashed lines are 90 percent confidence intervals obtained by employing Kilian’s (1998)

technique. Since our model involves six variables, 36 impulse response functions are

obtainable. The fully obtained Impulse Response Function (IRF) graphs and table are

documented in the annex, yet, analysis will focus only on impulse responses to a

monetary policy and credit shock under both specifications; (1) recursive identification

and (2) the theory based identification strategy.

i. Monetary Base as a Measure of Monetary Policy Stance (Model B)

In figure A, the left block defines a positive monetary shock of one standard deviation to

the monetary base in model B, employing recursive identification strategy. The effect on

real output, price level, money demand, and credit supply is as indicated in the economic

theory. Output responds positively with ranges of 1 percent to 2 percent only after two periods with the response persisting for around six months. Prices respond negatively, yet

response remains humble and close to zero and credit’s response is humbly negative until

th the 6 P P period when it picks up and grows positively to revert to the baseline. The negative response of total private credit to the monetary base due to a contractionary

188 monetary policy decision supports the argument for the presence of a credit channel.

However, the nature of the outcome and dynamics is in contrast with the estimated

structural relationships in the short run that indicates a positive response of total private

credit to the increase in the monetary base.

Figure (A)

Response to Nonf actorized One S.D. Innov ations ± 2 S.E.

Response of LRGDP2000 to LCRDTPRVTOT Response of LCPI2000 to LCRDTPRVTOT Response of LRGDP2000 to LMBASE Response of LCPI2000 to LMBASE .15 .010 .15 .010

.10 .005 .10 .005

.05 .000 .05 .000

.00 -.005 .00 -.005

-.05 -.010 -.05 -.010 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Response of LM2 to LMBASE Response of LCRDTPRVTOT to LMBASE .010 Response of LMBASE to LCRDTPRVTOT Response of LM2 to LCRDTPRVTOT .012 .010 .04 .005 .008 .005 .02 .004 .000 .00 .000 .000 -.005 -.02 -.004 -.005

-.010 -.04 -.008 1 2 3 4 5 6 7 8 9 10 -.010 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Response of LREER2000 to LMBASE Response of LREER2000 to LCRDTPRVTOT .04 .04 .03 .03 .02 .02 .01 .01 .00 .00 -.01 -.01 -.02 -.02 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

The response to a credit shock is presented in the right block of figure A. Real output shows a clear negative response in the second period that converges to zero in the third period. There is a minor short-term positive on money demand and a negative impact on

the monetary base until period 3 when it picks up and increases to converge back to zero

th by the 7 P P period. This is accompanied by an immediate—relative—increase in the price

level for a three months’ horizon when it converges back to zero and slightly declines for

the remaining forecast horizon. The model dynamics accordingly suggest that the

th increase in bank credit will have minimal impact on deposits until the 5 P P period, when

189 deposits show some positive response. Overall, impulse responses of the B model present results that reinforce the presence of a credit channel, as proposed also by the estimated structural relationships of the credit supply function. The monetary base initially responds negatively to credit shock then picks up and grows according to the impulse responses.

The estimated structural coefficient points to a significant but negative impact that fits the economic theory. ii. Lending Rate as a Measure of Monetary Policy Stance (Model R)

The IRF for the R model is illustrated in figure B. It follows different dynamics than that of the B model. Notably, in the left block which indicates the response to a monetary

th shock, the real output is almost unaffected through the 4 P P period when it drops remarkably following the interest rate shock (monetary policy shock). A lagged response of real output to monetary policy fits the economic theory. Money demand is positively affected by contemporaneous interest rate shocks and up to three months lag o. Prices immediately drop in response to the monetary policy shock. The money demand responses pursue a reverse direction unlike the estimated ones. Likewise, credit supply demonstrates a structural shift as it shows a slow increase up to period three then maintains a steady state for the remaining forecast horizon. These outcomes are indicative of the existence of a credit channel for monetary policy transmission that is operative in Egypt for the period under consideration. The right block shows responses to a credit shock. The real output responds negatively in the second period before it reverts and breaks even during period three, and then reacts neutrally for the rest of the time horizon. Although the negative response is relatively small, it is unconventional and merits further research in the future that focuses on the nature of credit constraints to the

190 private sector and households on the micro-level. Price levels and demand for money

confirm both the economic theory and the estimated coefficients in the structural model.

Figure (B)

Response to Nonf actorized One S.D. Innov ations ± 2 S.E.

Response of LRGDP2000 to LENDRT Response of LCPI2000 to LENDRT Response of LRGDP2000 to LCRDTPRVTOT Response of LCPI2000 to LCRDTPRVTOT .15 .015 .15 .015

.10 .010 .10 .010

.05 .005 .05 .005

.00 .000 .00 .000

-.05 -.005 -.05 -.005

-.10 -.010 -.10 -.010 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 10 Response of LENDRT to LCRDTPRVTOT Response of LM2 to LENDRT Response of LCRDTPRVTOT to LENDRT Response of LM2 to LCRDTPRVTOT .3 .012 .015 .012

.2 .008 .010 .008

.1 .004 .005 .004

.0 .000 .000 .000

-.1 -.004 -.005 -.004

-.2 -.008 -.010 -.008 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Response of LREER2000 to LENDRT Response of LREER2000 to LCRDTPRVTOT .04 .04

.02 .02

.00 .00

-.02 -.02

-.04 -.04 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

The overall outcome of the impulse response analysis reinforces the argument that we

raised in the structural model analysis section. There is statistical evidence that the credit

channel for monetary policy exists in Egypt during the period under study. However,

market imperfections still influence the dynamics of monetary aggregates and the real

sector, and reflect on reversals in anticipated reaction to monetary policy and credit shocks,

especially on the response of real output to credit shocks when considering lending rate as

an operational target. Results of the monetary base model seem more plausible and seem

closer to real monetary policy decisions in Egypt.

191 9. Variance Decomposition

Several issues arise when economists want to investigate the sources of cyclical

fluctuations, the first of which is the identification of structural shocks and their dynamic

effects, both of which are not directly observable. The SVAR literature offers a multitude

of possibilities for identifying, for example, supply, demand, technology, monetary policy, etc. shocks and the dynamic response of macroeconomic variables to them. A

method is needed to determine the contribution of different structural shocks. Variance

decomposition is one such method. The variance decomposition indicates the amount of

information each variable contributes to the other variables in the auto-regression. It

determines how much of the forecast error variance of each of the variables can be

explained by exogenous shocks to the other variables.

As we adopted a structural orthogonalisation based strategy for factorization, the forecast

standard errors are accordingly identical to those from the Cholesky factorization, since

the SVAR is just identified. The results for a ten-month horizon for B and R models are presented in table and graph formats in the appended annexes. It is generally observed

that for both models all shocks that correspond to each variable’s own shocks are

determinants for their forecast errors and statistically significant. This is particularly

higher in the case of the B model compared to the R model (see tables 20 for the B model

and table 27 for the R model).

i. Monetary Base as a Measure of Monetary Policy Stance (Model B):

The output shock accounts for 71.9 percent of total variation in real output at the end of a

ten-month horizon, whereas the aggregate supply shock accounts for 5.9 percent for the

192 same period. As for the monetary policy shock—that is the monetary base shock—it

strengthens at period 4 to read 4.9 percent with a 10 percent significance level and

strengthens to account for 9.4 percent at the end of the ten-month horizon at a

comfortable 5 percent significance level. This outcome provides additional evidence on

the significance of the credit channel for monetary policy. However, the limited influence

can be interpreted by the credit market distortion and underdevelopment and, in Egypt,

the impact of the large budget deficit. This impairs the efficiency of financial

intermediation to influence growth in real output.

The variability in prices is influenced by the aggregate supply shocks throughout the

whole forecast period of ten months (78.4 percent). Money demand and the monetary policy contribute to 10 percent and 8 percent of price variation indicating the effect of

monetary policy (defined by the monetary base) through the credit channel. On the other

hand, the monetary policy shock has sound and significant effects on real output (13.8 percent) price levels (14.6 percent) whereas it accounted for 27.4 percent) of money

demand. This indicated the critical role for the operational target in influencing the real

economy, price levels, and money demand, voting for the existence of the credit channel

of monetary transmission mechanism.

Monetary policy shock has sound effects on the monetary base and, in turn, influenced to

a lesser extent both the aggregate demand and supply shocks—at the same magnitude—

that was relatively impactful, reading around 14 percent, and that was statistically

th significant at a 10 percent significance level at the 10 P P period. The effect was even less

from credit supply and real effective exchange rate shocks. The climax of the total private

th th credit read 3.3 percent at the 4 P P period then withers to 2.8 percent at the 10 P P period.

193 The aggregate demand shock has a humble impact on money demand particularly in the

long run, whereas monetary policy shock seems not to influence money demand

throughout the period under study. Lastly, money demand shock demonstrates a slow and

unimportant impact to total private credit, thereby revealing a non-operative role in

explaining credit variations.

The total private credit influence heavy variations in the demand for money, calling for

immediate 10 percent statistically significant variations in the first month that scales up to

51.7 percent of variations by the tenth month. Whereas the real effective exchange rate

accounted for 10.7% and real output accounted for 7% of nominal money demand

variations by tenth month.

Overall, the outcomes of the analysis on variance decomposition reconfirm the existence

of the credit channel for monetary policy. However, the effectiveness of the channel in propagating monetary policy shocks remains modest. Authorities can always resort to a

monetary base as an operational target, as needed. Given the sound quantitative

foundations of this research, it will be feasible in future research to assess the

determinants and factors that would contribute to making this channel more effective

while adopting the monetary base as an operational target which the CBE has already

announced.

ii. Model R: Lending Rate as a Measure of Monetary Policy Stance

When considering the lending rate as the operating target, the output shock’s contribution

to the variation in output is a bit lower than that for the case for a monetary base standing

at 68 percent, whereas the lending rate’s contribution to the variation of output stands at

194 almost 12 percent with a 5 percent significance level. Money demand also shows a

contribution of 9 percent to variation of output at an almost 1 percent significance level.

This indicates a stronger propagation of monetary policy to real output. Total private

credit contributed to 6.9 percent of variation of real output at a 10 percent significance

rd th level at the 3P P period. The effect scales down to 5.4 percent at the end of the 10 P P period.

The lending rate contributes to 10 percent of the variation of price, which is 2 percent

higher than in the case of the monetary base. The same holds true for the total private

credit which scales up during the period under consideration and contributes to 4 percent

at the eighth month then diminishes to 3.7 percent in the tenth month.

The contribution of price level to money demand variation remains higher under model B

than under model R. However, the contribution of lending rate—operating target of

th monetary policy—contributed to 16 percent of variation of money demand at the 10 P P period. Real exchange rate and real output contributed to it minimally.

The variance decomposition of total private credit for the total private credit function

remains the same under the lending rate and the monetary base models.The total

contribution of private credit to the variation of the REER under the R model is 20 percent and statistically significant at 5 percent. This is double its contribution under the

B model. The same goes for the contribution of real output to variability of the REER under the R model reading 10 percent.

The overall results of the analysis indicate that the monetary policy shock under the R model has a strong influence in money demand at 15 percent and a moderate impact on real output of 10 percent with no impact on price level under the R model. This result is

195 higher than that of Awad 2010, where the contribution of the lending rate to real output

read 2.2 percent at six months and 2 percent at twelve months considering a shorter time

span with lower frequency data. This impact is stronger under the monetary base model,

where the monetary policy shock contributed to 27 percent of money demand, 15 percent

of price level and 14 percent of real output all statistically significant at a 5 percent

significance level. This clearly acknowledges the existence of the credit channel for

monetary policy and the role of the monetary base as an operating target as a reliable

sources of macroeconomic variation, Yet, further research in the future needs to focus on

identifying a means of fostering the effectiveness of this channel.

10. Conclusion

This chapter bridges the gap in the literature on less developed economies and

empirically investigates the presence of a credit channel for monetary policy transmission

in Egypt. It presents rigorous evidence-based answers to the proposed research questions

regarding the credit channel for a monetary transmission mechanism in Egypt. It

compared the two key operating targets that the CBE announced in 2010, namely, the

monetary base and the lending rate. The key findings indicate that the monetary base

model provides robust results that are in line with the theory that confirms the presence of

a credit channel for monetary policy. Results of the interest rate model reach the same

conclusion but with weaker magnitude on the real economy. The results of the model presented in this research indicate a stronger impact for the lending rate on real output

than earlier research that covered a shorter horizon and lower frequency data. The chapter

196 followed a robust framework to employ SVAR to model monetary policy strategy for

Egypt.

Our findings confirm the existence of a statistically significant credit channel for monetary policy when considering the monetary base as an operating target. Total private credit varies positively with monetary base, thereby indicating an operative credit channel from this perspective. This reflects the monetary authority’s decision to move from a quantitative operational target (excess reserves) to a price target (overnight inter-bank rate), launching a corridor system in June 2005, and issuing central bank instruments for the first time in August 2005 as the primary instruments for liquidity management through open market operations that influence the monetary base. The date didn’t vote for the adoption of the lending rate as an operational target. The negative, although insignificant, coefficient of total private credit in response to the lending rate doesn’t support the credit view for a transmission channel when considering the lending rate as an operating target. The overall outcome of the impulse response analysis reinforces the argument we raised in the structural model analysis section. There is statistical evidence that the credit channel for monetary policy exists in Egypt during the period under study.

However, market imperfections still influence the dynamics of monetary aggregates and the real sector and reflect on reversals in anticipated reaction to monetary policy and credit shocks especially on the response of real output to credit shocks when considering the lending rate as an operational target. Results of the monetary base model seem more

197 27 plausible and appear closer to real monetary policy decisions in Egypt. P26F P Variance

decomposition re-confirms the existence of the credit channel for monetary policy.

However, the effectiveness of the channel in propagating monetary policy shocks remains

modest. Authorities can always resort to the monetary base as an operational target, as

needed. Given the sound quantitative foundations of this research, it will be feasible in

future research to assess the determinants and factors that would contribute to making

this channel more effective while adopting the monetary base as an operational target

which the CBE already announced.

27 This is consistent with earlier findings by El-Shazly (2005) exercise to assess the empirical significance of credit rationing disequilibrium in Egypt. This exercise concluded that loan supply is not found an increasing function of the return on intermediated credit. Also, expectations on the state of the economy appear to explain the changing pattern of credit rationing over time.

198 References

1 Abdel-Baki, M. (2010).Alterations in monetary transmission mechanism in Egypt in the wake of the triple-F crisis, Journal of Investment management and financial innovations: vol. 7 (2), pages 217 – 227 . 2 Agenor, P. R, 1995. Monetary shocks and exchange rate dynamics with informal currency markets, International Review of Economics & Finance, Elsevier, vol. 4(3), pages 211-226 . 3 Agenor, P. R. and A. Koray, 2009.Monetary Shocks and Central Bank Liquidity with Credit Market Imperfections, Centre for Growth and Business Cycle Research Discussion Paper Series 120, Economics, The Univeristy of Manchester. 4 Agenor, P. R. and K. El Aynaoui, 2008."Excess Liquidity, Bank Pricing Rules, and Monetary Policy," Centre for Growth and Business Cycle Research Discussion Paper Series 105, Economics, The Univeristy of Manchester . 5 AK Kashyap, JC Stein, DW Wilcox; Monetary Policy and Credit Conditions: Evidence from the Composition of External Finance. American Economic Review, 83 (1993), pp. 78–98 ) 6 Al-Mashat R. and A. Billmeier, (2007), The Monetary Transmission Mechanism in Egypt, IMF Working Paper, No. 285. 7 Amisano, G., & Giannini, C. (1997). Topics in Structural VAR Economics. Berlin et. al. 8 Andrés, J., Ballabriga, F., & Vallés, J. (2000). Monetary policy and exchange rate behavior in the fiscal theory of the price level (No. 0004). Banco de España.Dedola and Lippi (2000) 9 Awad, I., (2010). The Monetary Targeting Regime in Egypt: Theoretical and

Empirical Investigations, Economic37TU Studies journal ,U37T Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 150-164. 10 Baoline, C., (2007), An Empirical Comparison of Methods for Temporal Distribution and Interpolation at the National Accounts, BEA Papers 0077, Bureau of Economic Analysis. 11 Ben S. Bernanke, J. Boivin, P. Eliasz; (2005), Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics, 120 (1)), pp. 387–422. 12 Bernanke, B. S. (1986, November). Alternative explanations of the money-income correlation. In Carnegie-Rochester conference series on public policy (Vol. 25, pp. 49-99). North-Holland. 13 Bernanke, B. S. (2007). The financial accelerator and the credit channel. Speech, June, 15. Available at:

http://www.federalreserve.gov/newsevents/speech/ber37TU nanke20070615a.htm U37T 14 Bernanke, B. S., & Blinder, A. S., (1988), Credit, Money, And Aggregate Demand, The American Economic Review; May; 78, 2; p. 435-439 15 Bernanke, B. S., & Blinder, A. S., (1992). The federal funds rate and the channels of monetary transmission. The American Economic Review, 901-921. )

199 16 Bernanke, B., M. Gertler, (1995), Inside the Black Box: The Credit Channel of Monetary Policy Transmission, Journal of Economic Perspectives, No. 9 (4), pp. 27-48. 17 Blanchard, D. Quah, (1989), The dynamic effects of aggregate demand and aggregate supply disturbances. American Economic Review, 79 pp. 655–673 18 Blanchard, O. J., & Watson, M. W. (1986). Are business cycles all alike?. In The American business cycle: Continuity and change (pp. 123-180). University of Chicago Press. 19 Blinder, A. S., & Stiglitz, J. E. (1983). Money, credit constraints, and economic activity (No. w1084). National Bureau of Economic Research.Blinder’s (1988 ( 20 Boivin, J., Kiley, M. T., & Mishkin, F. S. (2010). How has the monetary transmission mechanism evolved over time? (No. w15879). National Bureau of Economic Research. 21 Cheng, K., 2006. A VAR Analysis of Kenya's Monetary Policy Transmission Mechanism: How Does the Central Bank's REPO Rate Affect the Economy?, IMF Working Paper WP/06/300 (Washington: International Monetary Fund .( 22 Chow, G. C., & Lin, A. L. (1971). Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. The review of Economics and Statistics, 372-375. 23 Christiano LJ, M., Eichenbaum and CL. Evans, (1999). Monetary policy shocks: what have we learned and to what end? In Handbook of Macroeconomics, Vol. 1, ch. 2, Taylor JB, Woodford M (eds). Elsevier: Amsterdam; 65–148. 24 Ciccarelli, M., Maddaloni, A., & Peydró, J. L. (2010). Trusting the bankers: A new look at the credit channel of monetary transmission. European Central Bank (ECB) working paper series, (1228). ) 25 Čihák, M., Demirgüč-Kunt, A., Feyen, E., & Levine, R. (2013). Financial development in 205 economies, 1960 to 2010 (No. w18946). National Bureau of Economic Research. 26 Dave, C., Dressler, S. J., & Zhang, L. (2013). The bank lending channel: a FAVAR analysis. Journal of Money, Credit and Banking, 45(8), 1705-1720. 27 Doornik, J. A., & Hendry, D. F. (2009). Empirical Econometric Modelling: PcGive 13. Volume I, Timberlake Consultants LTD, London. 28 Dornbusch, R. (1976). Expectations and exchange rate dynamics. The journal of political economy, 1161-1176. 29 Ehrmann, M., & Worms, A. (2001). Interbank lending and monetary policy transmission: evidence for Germany (No. 2001, 11). Discussion paper Series 1/Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank. 30 El Mossallamy, M., T., Moursi , and E. Zakareya, (2007). Effect of Some Recent Changes in Egyptian Monetary Policy: Measurement and Evaluation. Middle East Business and Economic Review (MEBER), Vol. 19, No. 2 . 31 El-Shazly, A., (2015). Structural breaks and monetary dynamics: A time series analysis. Economic Modelling, Volume 53, February 2016, Pages 133-143 32 El-Shazly, A., (2005). Imperfect information and credit rationing equilibrium: evidence from Egypt. Review of Middle East Economics and Finance, Volume 3, Issue 2. 33 Favero, C. A., (2001). Applied macro econometrics. Oxford University Press

200 34 Fisher, I., (1930). The Theory of Interest. New York: The Macmillan Co. 35 Gerlach, S., & Smets, F. (1995). The monetary transmission mechanism: evidence from the G-7 countries. 36 Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica 37 (3): 424–438. 37 Hicks, J. R. [1935] 1951. A Suggestion for Simplifying the Theory of Money. Economica 2:1-19. Reprinted in Lutz and Mints, eds. 1951. 38 Ismail, M., Abdelmegeed, S. and Youssef, N. (2006), Temporal Disaggregation of Some Egyptian Time Series, Information & Decision Support Center (IDSC) 39 Jean Boivin, Marc P Giannoni, Ilian Mihov. (2009) Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data. American Economic Review 99350-384. Online publication date: 1-Mar-2009 40 Kamar, B., & Bakardzhieva, D. (2005). Economic trilemma and exchange rate management in Egypt. Review of Middle East Economics and Finance, 3(2), 91- 114. , 41 Kashyap, A.K. and J.C. Stein, (1995). The impact of monetary policy on bank balance sheets, Carnegie-Rochester Conference Series on Public Policy, 42, pp. 151-195. 42 Keating, J. (1992). Structural approaches to vector autoregressions. Federal Reserve Bank of St. Louis Review, 74(September/October 1992). 43 Kim, S., (2001), Monetary Policy, Foreign Exchange Intervention, and the Exchange Rate in a Unifying Framework, Journal of International Economics, No. 60 (2003), pp. 355-386. 44 Kim, S., and N. Roubini,(2000), Exchange Rate Anomalies in the Industrial Countries: A Solution with a Structural VAR Approach, Journal of Monetary Economics, vol.45, pp.561-586 . 45 Kim, S., and N. Roubini, (2000), Exchange Rate Anomalies in the Industrial Countries: A Solution with a Structural VAR Approach, Journal of Monetary Economics, vol.45, pp.561-586. 46 Litterman, R. B. (1983). A random walk, Markov model for the distribution of time series. Journal of Business & Economic Statistics, 1(2), 169-173. 47 Meltzer, A. H. (1995). Monetary, credit and (other) transmission processes: a monetarist perspective. The Journal of Economic Perspectives, 49-72. 48 Mishkin F., (1995), “Symposium on the Monetary Transmission Mechanism”, Journal of Economic Perspectives, Vol. 9. 49 Mishra, P., Montiel, P. J., & Spilimbergo, A. (2012). Monetary transmission in low- income countries: Effectiveness and policy implications. IMF Economic Review, 60(2), 270-302. 50 Morris, C. S., & Sellon, G. H. (1995). Bank lending and monetary policy: Evidence on a credit channel. Federal Reserve Bank of Kansas City Economic Review, 80(2), 59-75. 51 Mundell, R. (1963). Inflation and real interest. The Journal of Political Economy, 280-283. 52 Mundell, R. A. (1963). Capital mobility and stabilization policy under fixed and flexible exchange rates. Canadian Journal of Economics and Political Science/Revue canadienne de economiques et science politique, 29(04), 475-485.

201 53 Neaime S., (2007) “Monetary Policy Transmission and Targeting Mechanisms in the MENA Region”, Paper presented at the Economic Research Forum (ERF) 14th Annual Conference, Cairo, Egypt. 54 Omran, M., (2001) "Detecting the Performance Consequences of Privatizing Egyptian State-Owned Enterprises: Does Ownership Structure Really Matter?", 8th Annual Conference of the Economic Research Forum (ERF), Cairo, Egypt. 55 Papadamou, S., & Siriopoulos, C. (2012). Banks’ lending behavior and monetary policy: evidence from Sweden. Review of Quantitative Finance and Accounting, 38(2), 131-148. 56 Ramaswamy, R., & Sloek, T. (1998). The real effects of monetary policy in the European Union: What are the differences?. Staff Papers-International Monetary Fund, 374-396. 57 Roosa, R. V. (1951). Interest rates and the central bank. Money, Trade, and Economic Growth: Essays in Honor of John Henry Williams. 58 Shapiro, M., & Watson, M. (1988). Sources of business cycles fluctuations. In NBER Macroeconomics Annual 1988, Volume 3 (pp. 111-156). MIT Press. 59 Sims, C. A. (1986). Are forecasting models usable for policy analysis?. Federal Reserve Bank of Minneapolis Quarterly Review, 10(1), 2-16. 60 Sims, C. A., & Zha, T. (1998). Bayesian methods for dynamic multivariate models. International Economic Review, 949-968. 61 Sims, C. A., & Zha, T. A. (1998). Does monetary policy generate recessions? (No. 98-12). working paper, Federal Reserve Bank of Atlanta. 62 Tobin, J. (1969). A general equilibrium approach to monetary theory. Journal of money, credit and banking, 1(1), 15-29. 63 Youssef, S., (1994), Egyptian State Enterprises: A Sector in Transition”, International Journal of Commerce and Management. 64 Youssef, S., (1996). Structural Reform Program of Egyptian State Owned Enterprises, Current Impact and Future Prospects, Journal of Management Development, Vol. 15, No.5. 65 Ziaei, S. M. (2012). Transmission Mechanisms of Monetary Policy in Saudi Arabia: Evidence from SVAR Analysis. Journal of Modern Accounting and Auditing. 8(7), 990-1012.

202

Annex VII: Summary of Findings of the Empirical Research on the

Credit Channel as a Monetary Transmission Mechanism in Egypt

203 Table (12): Summary of Findings of Empirical Research on the Credit Channel as a Monetary Transmission Mechanism in Egypt :

Al-Mashat and Billmeier Awad Abdel-Baki (2007) (2010) (2010)

Endogenous • Egyptian GDP • Egyptian GDP • Egyptian GDP • Whole Sale Price Index • Whole Sale Price Index • Consumer Price Index • Monetary policy stance • Deposit Rate (CPI) (MPS) estimated by • N2 • Interbank Policy Rate Moursi, Mossallamy and • USD Exchange Rate (PR) Zakareya (2007) • M2 • Nominal Effective • Foreign exchange Exchange Rate (NEER) intervention (FX) • Nominal Effective Exchange Rate (NEER)

Exogenous • federal reserve’s fund rate • federal funds rate • composite food and oil • international oil prices • US GDP prices (FO) (simple average of the • US CPI spot prices for dated Brent, West Texas Intermediate, and the Dubai Fateh) Coverage 1996–2005 1995–2007 2003–March 2010

Frequency Monthly Quarterly Quarterly

Extrapolated data GDP: from annual and GDP: from annual to None series quarterly to monthly quarterly

Identification method Cholesky decomposition of Cholesky decomposition of Restrictions to the of structural the covariance matrix of the the covariance matrix of the covariance matrix based on innovations error terms error terms theory

204

Annex VIII: Summary Tables and Integration Tests

205 Table (13a): Data Description for Credit Channel for Monetary Policy Model:

Acronym Description Source Adjusted Date Accessed

Annual frequency was obtained from IMF International Financial Yes Log of real Gross th RGDP Statistics then August 25 P P 2015 Domestic Product interpolated to (log of levels) monthly frequency using Chow- Lin method via Matlab

Yes IMF Log Consumer Price International th CPI August 25 P P 2015 Index Financial (log of levels Statistics deflated by CPI: base July 2010)

IMF International th Average Lending Rate Average Lending Rate No August 25 P P 2015 Financial Statistics

IMF Yes International th Monetary Base Log of Monetary Base August 25 P P 2015 Financial Statistics (log of levels)

Log of Broad Money IMF Yes (including money in International th M2 August 25 P P 2015 circulation, demand and Financial time saving deposits) Statistics (log of levels)

IMF Yes Log of Credit to Private International th Private Credit August 25 P P 2015 Sector and Households Financial Statistics (log of levels)

Yes Real Effective IMF Log of Real Effective International th August 25 P P 2015 Exchange Rate Financial (log of levels, base Exchange Rate Statistics July 2010))

206 Table (13b): Summary Statistics:

Sample: June 1997 - June 2013 (193 observations; monthly data) # of Variables: 7

Variable Leading sample #obs #miss Minimum Mean Maximum std.dev

June 1997 - June 193 0 7.8883 8.6552 9.4814 0.39507 RGDP 2013

June 1997 - June 193 0 4.5076 4.9538 5.649 0.36279 CPI 2013

Average June 1997 - June 193 0 10.6 12.672 14.3 0.85054 Lending Rate 2013

June 1997 - June 193 0 4.1759 5.1128 5.8927 0.55058 Monetary Base 2013

June 1997 - June 193 0 5.2673 6.1941 7.1671 0.57279 M2 2013

June 1997 - June 193 0 4.6803 5.6062 6.2096 0.38702 Private Credit 2013

Real Effective June 1997 - June 193 0 4.1431 4.4377 4.6501 0.13505 Exchange Rate 2013

Table (14a): Augmented Dickey-Fuller Tests for Unit Roots: First Differences of Log Levels of Real Data - Sample June 1997 - June 2013

Maximum Variable t-adf beta Y_lag t-DY_lag AIC Lags

RGDP -18.76** -1.1679 7.150 1 -4.108

CPI -5.341** 0.25183 1.346 8 -10.10

Average Lending -15.83** -0.18513 0 -3.131 Rate

Monetary Base -11.94** 0.082952 0 -6.124

M2 -12.52** 0.042024 0 -9.816

Private Credit -3.915* 0.51802 -1.517 4 -9.630

Real Effective -6.496** 0.23361 -1.786 2 -7.518 Exchange Rate

Constant,Trend and Seasonal Dummies Included - Critical Values; 5%=-3.44 1%=-4.01

P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

207 Table (14b): Augmented Dickey-Fuller Tests for Unit Roots: Levels of Real Data - Sample June 1997 - June 2013

t-adf beta Y_lag t-DY_lag Maximum Lags AIC Variable

RGDP -2.202 0.87814 -6.362 2 -4.126

CPI -1.665 0.99057 -1.238 8 -10.11

Average Lending Rate -1.797 0.95156 -2.151 1 -3.139

Monetary Base -1.311 0.97815 0 -6.127

M2 -1.264 0.98149 0 -9.824

Private Credit -3.49 0.96123 2.527 2 -9.684

Real Effective Exchange Rate -1.521 0.98152 1.912 3 -7.521

Constant,Trend and Seasonal Dummies Included - Critical Values; 5%=-3.44 1%=-4.01

P-values are in brackets;

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

208

Annex IX: Diagnostic Tables for Recursive VAR Identification Strategy

(Cholesky Decomposition): Monetary Base Model (Model (B))

209 Table (15a): Monetary Base Model (Model (B)): Lag Structure and Reduction Tests, June 1997 - June 2013

Significance of Each Lag Joint AIC SC F Test significanc Restrict e of all lags 17 16 15 14 13 12 ed Lags

0.72222 0.87903 0.64387 0.90541 0.93790 0.95337 0.72222 -23.46 -11.14 F(36,253) 17 [0.8797] [0.6697 [0.9437] [0.6275] [0.5748] [0.5496] [0.8797]

0.98299 0.66794 1.0236 0.98316 1.0792 0.98299 -23.44 -11.77 F(36,279) 16 [0.5015] [0.9281] [0.4374] [0.3553] [0.5015] [0.5012]

0.99387 1.2587 0.85894 1.1744 0.99387 -23.32 -12.30 F(36,305) 15 [0.4836] [0.1551] [0.7023] [0.2349] [0.4836]

1.3437 0.76002 1.1931 1.3437 -23.25 -12.87 F(36,332) 14 [0.0964] [0.8406] [0.2138] [0.0964]

1.3535 1.1614 -23.06 -13.36 F(36,358) 13 [0.0901] [0.2472]

1.6933 -22.91 -13.83 F(36,384) 12 [0.0091]**

P-values are in brackets ; *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

The Monetary Base Model’s VAR System is presented as follows:

Lrgdp2000 Lrgdp2000 , Lcpi2000 , Lcpi2000 Lmbase = + ∑ , Lmbase . + dumm200712 + dumm201301 Lm2 Lcrdtprvtot , Lcrdtprvtot + Centered SeasonalLreer2000 Dummies, + Lreer2000 Where:

GDP, CPI, Monetary Base, M2, Credit to Private Sector and Real Effective Exchange Rate are in Logs dummXXXX: dummy variable (year/month) : error term

210 Table (15b): Residual Diagnostics Tests for the - 13 Lags VAR- Monetary Base Model

Single-equation diagnostics using reduced-form residuals: Lrgdp2000 : Portmanteau: degrees of freedom correction >= n^2*lag Lrgdp2000 : AR 1-7 test: F(7,77) = 2.5084 [0.0224]* Lrgdp2000 : ARCH 1-7 test: F(7,163) = 0.93723 [0.4794] Lrgdp2000 : Normality test: Chi^2(2) = 1.8963 [0.3875] Lrgdp2000 : Hetero test: not enough observations Lcpi2000 : Portmanteau: degrees of freedom correction >= n^2*lag Lcpi2000 : AR 1-7 test: F(7,77) = 0.46530 [0.8567] Lcpi2000 : ARCH 1-7 test: F(7,163) = 2.2978 [0.0293]* Lcpi2000 : Normality test: Chi^2(2) = 0.96446 [0.6174] Lcpi2000 : Hetero test: not enough observations Lmbase : Portmanteau: degrees of freedom correction >= n^2*lag Lmbase : AR 1-7 test: F(7,77) = 0.85183 [0.5483] Lmbase : ARCH 1-7 test: F(7,163) = 0.21889 [0.9806] Lmbase : Normality test: Chi^2(2) = 24.051 [0.0000]** Lmbase : Hetero test: not enough observations Lm2 : Portmanteau: degrees of freedom correction >= n^2*lag Lm2 : AR 1-7 test: F(7,77) = 0.97320 [0.4569] Lm2 : ARCH 1-7 test: F(7,163) = 0.095074 [0.9985] Lm2 : Normality test: Chi^2(2) = 65.780 [0.0000]** Lm2 : Hetero test: not enough observations Lcrdtprvtot : Portmanteau: degrees of freedom correction >= n^2*lag Lcrdtprvtot : AR 1-7 test: F(7,77) = 1.2377 [0.2928] Lcrdtprvtot : ARCH 1-7 test: F(7,163) = 0.19762 [0.9856] Lcrdtprvtot : Normality test: Chi^2(2) = 17.863 [0.0001]** Lcrdtprvtot : Hetero test: not enough observations Lreer2000 : Portmanteau: degrees of freedom correction >= n^2*lag Lreer2000 : AR 1-7 test: F(7,77) = 0.51122 [0.8234] Lreer2000 : ARCH 1-7 test: F(7,163) = 0.38540 [0.9099] Lreer2000 : Normality test: Chi^2(2) = 24.674 [0.0000]** Lreer2000 : Hetero test: not enough observations

Portmanteau: degrees of freedom correction >= n^2*lag Vector AR 1-7 test: F(252,228)= 1.1217 [0.1883] Vector Normality test: Chi^2(12) = 109.06 [0.0000]** ZHetero test: not enough observations Vector RESET23 test: F(72,370) = 2.3437 [0.0000]**

211 Table (16): Structural VAR Estimates- 13 Lags VAR- Monetary Base Model (Recursive Identification)

Structural VAR Estimates Date: 10/10/15 Time: 03:39 Sample (adjusted): 1998M07 2013M06 Included observations: 180 after adjustments Estimation method: method of scoring (analytic derivatives) Convergence achieved after 1 iterations Structural VAR is just-identified

Model: Ae = Bu where E[uu']=I Restriction Type: short-run pattern matrix A = 1 0 0 0 0 0 C(1) 1 0 0 0 0 C(2) C(6) 1 0 0 0 C(3) C(7) C(10) 1 0 0 C(4) C(8) C(11) C(13) 1 0 C(5) C(9) C(12) C(14) C(15) 1 B = C(16) 0 0 0 0 0 0 C(17) 0 0 0 0 0 0 C(18) 0 0 0 0 0 0 C(19) 0 0 0 0 0 0 C(20) 0 0 0 0 0 0 C(21)

Coefficient Std. Error z-Statistic Prob.

C(1) 0.003710 0.003753 0.988400 0.3230 C(2) -0.007093 0.025125 -0.282323 0.7777 C(3) -0.006001 0.003743 -1.602949 0.1089 C(4) -0.003388 0.003492 -0.970243 0.3319 C(5) 0.015064 0.012738 1.182603 0.2370 C(6) 1.234726 0.497625 2.481237 0.0131 C(7) -0.024664 0.075383 -0.327187 0.7435 C(8) 0.182856 0.069850 2.617816 0.0088 C(9) -0.867142 0.258895 -3.349392 0.0008 C(10) 0.002372 0.011103 0.213619 0.8308 C(11) 0.024991 0.010286 2.429614 0.0151 C(12) 0.056088 0.038028 1.474909 0.1402 C(13) -0.313290 0.069045 -4.537504 0.0000 C(14) 0.494895 0.265147 1.866490 0.0620 C(15) 0.794863 0.271147 2.931483 0.0034 C(16) 0.108365 0.005711 18.97367 0.0000 C(17) 0.005457 0.000288 18.97367 0.0000 C(18) 0.036430 0.001920 18.97367 0.0000 C(19) 0.005427 0.000286 18.97367 0.0000 C(20) 0.005027 0.000265 18.97367 0.0000 C(21) 0.018287 0.000964 18.97367 0.0000

Log likelihood 3013.718

Estimated A matrix: 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.003710 1.000000 0.000000 0.000000 0.000000 0.000000 -0.007093 1.234726 1.000000 0.000000 0.000000 0.000000 -0.006001 -0.024664 0.002372 1.000000 0.000000 0.000000 -0.003388 0.182856 0.024991 -0.313290 1.000000 0.000000 0.015064 -0.867142 0.056088 0.494895 0.794863 1.000000 Estimated B matrix: 0.108365 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.005457 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.036430 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.005427 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.005027 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.018287

212

Annex X: Graphical Representation and Diagnostics for Recursive VAR

Identification Strategy (Cholesky Decomposition): Monetary Base

Model (Model (B))

213 Graph (20): Plot of Logs of Levels and First Differences of Logs of Variables for- Monetary Base (Model (B)): 2010 2010 2010 2010 2010 2010 2005 2005 2005 2005 2005 2005 2000 2000 2000 2000 2000 2000 DLrgdp2000 DLrgdp2000 DLcpi2000 DLmbase DLm2 DLcrdtprvtot DLreer2000 0.5 0.0 0.2 0.1 -0.2 -0.1 0.04 0.00 0.05 0.00 0.025 -0.025 2010 2010 2010 2010 2010 2010 2005 2005 2005 2005 2005 2005 2000 2000 2000 2000 2000 2000 Lrgdp2000 Lrgdp2000 Lcpi2000 Lmbase Lm2 Lcrdtprvtot Lreer2000 9 8 7 6 6 5 5.5 5.0 5.5 4.5 4.6 4.2

214 Graph (21): Diagnostic Graphs of - 13 Lags- VAR Model: Monetary Base (Model (B)): 10 10 10 10 10 10 5 5 5 5 5 5 ACF-r:Lrgdp2000 ACF-r:Lrgdp2000 ACF-r:Lcpi2000 ACF-r:Lmbase ACF-r:Lm2 ACF-r:Lcrdtprvtot ACF-r:Lreer2000 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 2.5 5 2.5 2.5 2.5 0 N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) 0.0 0 0.0 0.0 -2.5 r:Lrgdp2000 r:Lrgdp2000 r:Lcpi2000 r:Lmbase r:Lm2 r:Lcrdtprvtot r:Lreer2000 -2.5 Density Density Density Density Density Density -2.5 -5 -2.5 -5 0.4 0.2 0.4 0.2 0.6 0.2 0.4 0.2 0.4 0.2 0.4 0.2 2010 2010 2010 2010 2010 2010 r:Lrgdp2000 (scaled) (scaled) r:Lrgdp2000 (scaled) r:Lcpi2000 (scaled) r:Lmbase (scaled) r:Lm2 (scaled) r:Lcrdtprvtot (scaled) r:Lreer2000 2000 2000 2000 2000 2000 2000 2 0 1 2 1 2 -1 -2 -1 -2 2.5 -2.5 2010 2010 2010 2010 2010 2010 Fitted Fitted Fitted Fitted Fitted Fitted Fitted Fitted Fitted Lrgdp2000 Lrgdp2000 Lcpi2000 Lmbase Lm2 Lcrdtprvtot Lreer2000 2000 2000 2000 2000 2000 2000 7 6 6 5 9.5 8.5 5.5 5.0 5.5 4.5 4.6 4.2

215 Graph (22): Scaled Residuals - 13 Lags- VAR Model: Monetary Base (Model (B)):

0.50 r Lrgdp2000 0.01 r Lcpi2000 0.25

0.00 0.00

-0.25 -0.01 2000 2005 2010 2000 2005 2010

r Lmbase r Lm2 0.1 0.02

0.0 0.00

-0.1 -0.02

2000 2005 2010 2000 2005 2010 0.10 r Lcrdtprvtot r Lreer2000 0.01 0.05

0.00 0.00

-0.05 -0.01

2000 2005 2010 2000 2005 2010

216 Graph (23): Stability Test (One Step and N Down Chow Test) - 13 Lags- VAR Model: Monetary Base (Model (B)): 1% 1% 2010 2010 2010 1% 1up Lm2 Ndn Ndn Lrgdp2000 Ndn Lcrdtprvtot 2000 2000 2000 1.0 0.5 0.0 1.0 0.5 1.0 0.5 2010 2010 2010 1% 1% 1% 1up Lmbase 1up CHOWs Ndn Lm2 2000 2000 2000 1.0 0.5 0.0 1.0 0.5 1.0 0.5 1% 1% 1% 2010 2010 2010 2010 1% 1up Lcpi2000 1up Lreer2000 NdnLmbase NdnCHOWs 2000 2000 2000 2000 1.0 0.5 0.0 1.5 1.0 0.5 0.0 1.0 0.5 1.0 0.5 1% 1% 1% 1% 2010 2010 2010 2010 1up Lrgdp2000 1up Lcrdtprvtot NdnLcpi2000 NdnLreer2000 2000 2000 2000 2000 1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 1.0 0.5

217

Annex XI: Structural VAR Estimates: Non - Recursive VAR

Identification Strategy (Theory Based Restrictions): Monetary Base

Model (Model (B))

218 Table (17): Structural VAR Estimates- 13 Lags VAR- Monetary Base Model (Model (B)): (Non Recursive Identification: Theory Based Restrictions)

Structural VAR Estim ates D ate: 10/10/15 Tim e: 03:39 Sam ple (adjusted): 1998M07 2013M06 Included observations: 180 after adjustm ents Estim ation m ethod: m ethod of scoring (analytic deriva tives ) Convergence achieved after 1 iterations Structural VAR is jus t-identified

Model: Ae = Bu w here E[uu']=I Restriction Type: short-run pattern m atrix A = 1 0 0 0 0 0 C (1 ) 1 0 0 0 0 C(2) C(6) 1 0 0 0 C(3) C(7) C(10) 1 0 C(14) C(4) C(8) C(11) C(12) 1 C(15) C(5) C(9) 0 C(13) 0 1 B = C (1 6 ) 0 0 0 0 0 0 C (1 7 ) 0 0 0 0 0 0 C (1 8 ) 0 0 0 0 0 0 C (1 9 ) 0 0 0 0 0 0 C (2 0 ) 0 0 0 0 0 0 C(21)

Coefficient Std. Error z-Statistic Prob.

C(1) 0.003710 0.003753 0.988400 0.3230 C(2) -0.007093 0.025125 -0.282323 0.7777 C(3) 0.008590 0.036949 0.232484 0.8162 C(4) -0.002370 0.003429 -0.691207 0.4894 C(5) 0.109401 0.442478 0.247247 0.8047 C(6) 1.234726 0.497625 2.481237 0.0131 C(7) -0.677435 1.616176 -0.419159 0.6751 C(8) 0.124813 0.071055 1.756577 0.0790 C(9) -0.635800 2.331778 -0.272667 0.7851 C(10) 0.024998 0.061367 0.407347 0.6838 C(11) 0.027068 0.010074 2.686907 0.0072 C(12) -0.270644 0.069004 -3.922142 0.0001 C(13) -14.52858 73.29495 -0.198221 0.8429 C(14) 0.656619 1.616142 0.406288 0.6845 C(15) 0.057327 0.019555 2.931483 0.0034 C(16) 0.108365 0.005711 18.97367 0.0000 C(17) 0.005457 0.000288 18.97367 0.0000 C(18) 0.036430 0.001920 18.97367 0.0000 C(19) 0.012600 0.028079 0.448751 0.6536 C(20) 0.004911 0.000259 18.97367 0.0000 C(21) 0.084966 0.387999 0.218985 0.8267

Log likelihood 3013.718

Estim ated A m atrix: 1.000000 0.000000 0.000000 0.000000 0.000000 0.000 0 0 0 0.003710 1.000000 0.000000 0.000000 0.000000 0.000 0 0 0 -0.007093 1.234726 1.000000 0.000000 0.000000 0.000 0 0 0 0.008590 -0.677435 0.024998 1.000000 0.000000 0.656 6 1 9 -0.002370 0.124813 0.027068 -0.270644 1.000000 0.057 3 2 7 0.109401 -0.635800 0.000000 -14.52858 0.000000 1.000 0 0 0 Estim ated B m atrix: 0.108365 0.000000 0.000000 0.000000 0.000000 0.000 0 0 0 0.000000 0.005457 0.000000 0.000000 0.000000 0.000 0 0 0 0.000000 0.000000 0.036430 0.000000 0.000000 0.000 0 0 0 0.000000 0.000000 0.000000 0.012600 0.000000 0.000 0 0 0 0.000000 0.000000 0.000000 0.000000 0.004911 0.000 0 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.084 9 6 6

219

Annex XII: Granger Causality, Impulse Response and Variance

Decomposition (tables): Monetary Base Model (Model (B))

220 Table (18): Granger Causality Tables- 13 Lags VAR- Monetary Base Model: (Model (B)):

VAR Granger Causality/Block Exogeneity Wald Tests Date: 10/10/15 Time: 06:18 Sample: 1997M06 2013M06 Included observations: 180

Dependent variable: LRGDP2000

Excluded Chi-sq df Prob.

LCPI2000 24.44007 13 0.0273 LMBASE 15.08528 13 0.3021 LM2 15.62592 13 0.2699 LCRDTPRVT... 14.23439 13 0.3575 LREER2000 4.737655 13 0.9805

All 101.5620 65 0.0025

Dependent variable: LCPI2000

Excluded Chi-sq df Prob.

LRGDP2000 15.12115 13 0.2999 LMBASE 25.42409 13 0.0203 LM2 15.14954 13 0.2981 LCRDTPRVT... 23.08744 13 0.0406 LREER2000 9.430569 13 0.7397

All 103.4421 65 0.0017

Dependent variable: LMBASE

Excluded Chi-sq df Prob.

LRGDP2000 32.16327 13 0.0023 LCPI2000 21.61640 13 0.0616 LM2 41.64714 13 0.0001 LCRDTPRVT... 19.10824 13 0.1198 LREER2000 23.77881 13 0.0332

All 135.9347 65 0.0000

Dependent variable: LM2

Excluded Chi-sq df Prob.

LRGDP2000 25.82384 13 0.0180 LCPI2000 38.50705 13 0.0002 LMBASE 19.88352 13 0.0982 LCRDTPRVT... 13.60379 13 0.4023 LREER2000 18.84087 13 0.1281

All 122.7862 65 0.0000

221 Table (18): Granger Causality Tables- 13 Lags VAR- Monetary Base Model (Model (B)): (Cont’d):

Dependent variable: LCRDTPRVTOT

Excluded Chi-sq df Prob.

LRGDP2000 18.65676 13 0.1341 LCPI2000 29.40427 13 0.0057 LMBASE 9.146129 13 0.7618 LM2 32.14003 13 0.0023 LREER2000 36.61552 13 0.0005

All 130.5037 65 0.0000

Dependent variable: LREER2000

Excluded Chi-sq df Prob.

LRGDP2000 12.24258 13 0.5079 LCPI2000 26.13514 13 0.0163 LMBASE 14.19769 13 0.3601 LM2 26.39281 13 0.0150 LCRDTPRVT... 16.49201 13 0.2236

All 95.04003 65 0.0089

222 Table (19): Impulse Response Function Tables- 13 Lags VAR- Monetary Base Model (Model (B)): (Non Factorized One SD Deviation)

Response of LRGDP2000: Perio... LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.108365 0.000000 0.000000 0.000000 0.000000 0.000000 2 -0.003295 0.011478 0.009747 0.024178 -0.006250 -0.005505 3 0.001360 -0.021584 0.012930 -0.009741 -0.021493 0.002430 4 0.030489 0.022749 0.018860 -0.008044 -0.000191 -0.015016 5 0.007460 0.011021 0.011108 0.001371 -0.007426 -0.012706 6 0.009666 0.009997 0.013900 -0.009378 -0.004364 -0.009343 7 0.012752 0.017673 0.000551 -0.001356 -0.002782 -0.005373 8 0.006020 -0.003321 0.011830 0.003485 -0.007114 0.000500 9 0.024632 -0.002913 0.014617 -0.015490 -0.008443 0.002439 10 0.012601 0.011225 0.013368 -0.005197 -0.003248 -0.011077

Response of LCPI2000: Perio... LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.000000 0.005471 0.000000 0.000000 0.000000 0.000000 2 0.000179 0.006813 -0.000859 1.97E-05 2.09E-05 -0.000148 3 0.000173 0.006361 -0.001401 0.000121 0.000973 0.000606 4 0.000139 0.005714 -0.002350 0.001165 0.000252 0.000625 5 -0.000100 0.005268 -0.002474 0.001763 0.000167 0.000109 6 0.000656 0.004477 -0.002755 0.001890 -7.16E-05 -0.000125 7 0.000364 0.004971 -0.002076 0.002551 -0.000754 -0.000754 8 0.000394 0.004196 -0.001367 0.002404 -0.000981 -0.000766 9 0.000731 0.002943 -0.000655 0.002831 -0.000512 -0.001034 10 0.001534 0.001253 -0.000795 0.002882 -0.000221 -0.001416

Response of LMBASE: Perio... LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.000000 0.000000 0.037070 0.000000 0.000000 0.000000 2 0.002058 0.005329 0.031193 0.007028 -0.004349 -0.000133 3 -0.002726 0.000248 0.023896 0.014572 -0.010060 -0.001950 4 -0.001800 -0.003235 0.022006 0.020063 -0.007654 -0.001777 5 -0.005684 -0.004471 0.022577 0.018194 -0.001518 0.006900 6 0.008413 -0.012704 0.017618 0.019829 0.001964 0.010138 7 0.011940 -0.010129 0.010904 0.022086 -0.001131 0.007335 8 0.013648 -0.017333 0.011697 0.028730 -0.004083 0.004569 9 0.015521 -0.020287 0.009184 0.026238 -0.007038 0.004879 10 0.020794 -0.028947 0.006751 0.026393 -0.009578 0.005622

Response of LM2: Perio... LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.000000 0.000000 0.000000 0.005467 0.000000 0.000000 2 0.001093 -0.002344 -0.000245 0.005590 0.000105 -0.000453 3 6.96E-05 -0.001552 0.000703 0.006112 -0.000283 -0.000224 4 0.001114 -0.001723 0.000702 0.005502 -5.27E-05 -0.000135 5 0.001115 -0.002717 0.000428 0.005302 0.000545 -0.001216 6 0.000494 -0.002911 0.001207 0.005140 -6.66E-06 -0.003014 7 0.000411 -0.003013 0.000602 0.005464 0.000596 -0.003362 8 0.000698 -0.002301 0.000232 0.004551 0.000855 -0.003020 9 0.000667 -0.003041 0.000678 0.005434 0.001255 -0.002848 10 0.001036 -0.003288 -0.000783 0.006030 0.001184 -0.002503

Response of LCRDTPRVTOT: Perio... LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.000000 0.000000 0.000000 0.000000 0.005479 0.000000 2 0.000196 -0.000423 -0.000527 0.000971 0.003772 -0.000187 3 -0.000738 0.000326 -0.000728 0.001738 0.003989 -2.47E-06 4 0.000268 -0.000304 -0.001275 0.002698 0.003623 0.001190 5 0.000373 -0.002026 -0.000850 0.003531 0.003885 0.002480 6 0.000131 -0.002101 -0.001291 0.004572 0.003495 0.002666 7 0.000907 -0.002072 -0.000279 0.005339 0.003189 0.002592 8 0.001376 -0.002050 -0.000313 0.005596 0.003051 0.003873 9 0.002613 -0.002333 7.04E-05 0.006268 0.002839 0.003474 10 0.002709 -0.002789 -3.67E-05 0.006603 0.002187 0.002860

Response of LREER2000: Perio... LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.000000 0.000000 0.000000 0.000000 0.000000 0.020208 2 0.000675 0.003500 0.002431 -0.004998 -0.001526 0.023405 3 0.001204 0.001650 0.000530 -0.002740 -0.001948 0.021965 4 0.004098 -0.001153 -0.003149 0.000972 -0.003965 0.021017 5 0.004851 -0.001527 3.33E-05 0.000523 -0.003373 0.022691 6 0.008287 -0.002517 0.001521 0.000421 -0.003049 0.024050 7 0.008592 -0.000126 0.003045 0.002757 -0.004056 0.020626 8 0.007260 -0.005178 0.003115 0.004387 -0.003530 0.016731 9 0.007641 -0.007098 0.004310 0.002875 -0.003506 0.015687 10 0.008852 -0.006648 0.003940 0.003428 -0.004194 0.013681 Nonfactorized One Std.Dev.

223 Table (20): Variance Decomposition for the Reduced SVAR (Monetary Base (Model (B)) :

Variance Decomposition of LRGDP2000: Perio... S.E. LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.108365 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 (0.00000) (0.00000) (0.00000) (0.00000) (0.00000) (0.00000) 2 0.111952 93.70197 0.749602 0.897931 4.280315 0.172202 0.197983 (4.73546) (2.13860) (2.07565) (3.16161) (0.77745) (2.05535) 3 0.117957 84.40601 3.664766 2.738904 5.889787 3.087446 0.213085 (5.45511) (3.43570) (2.99151) (3.73352) (2.56476) (2.11190) 4 0.125202 80.90177 4.662095 4.888610 5.390228 2.790256 1.367041 (5.86525) (3.75476) (3.58815) (3.58095) (2.38243) (2.24408) 5 0.126907 79.15469 4.801033 5.800175 5.262092 2.830605 2.151406 (6.54289) (3.68084) (3.78431) (3.32995) (2.26215) (3.30808) 6 0.128836 77.30665 4.820865 7.007893 5.571966 2.774506 2.518120 (7.09142) (3.87652) (4.20703) (3.55980) (2.28127) (3.61223) 7 0.130516 76.14145 6.272802 6.839685 5.437002 2.716558 2.592506 (6.84470) (4.24892) (3.98849) (3.45303) (2.35021) (3.63880) 8 0.131526 75.20034 6.279385 7.675894 5.361540 2.928795 2.554048 (6.54139) (4.38974) (4.51522) (3.48082) (2.56554) (3.98562) 9 0.135935 73.08183 5.969603 8.554644 6.868200 3.108311 2.417409 (6.46449) (4.18604) (5.12494) (3.79586) (2.71605) (3.98389) 10 0.137857 71.92387 5.992780 9.418425 6.760236 3.025510 2.879176 (6.76497) (4.20275) (5.51298) (3.81070) (2.64679) (4.04078)

Variance Decomposition of LCPI2000: Perio... S.E. LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.005471 0.539812 99.46019 0.000000 0.000000 0.000000 0.000000 (1.59757) (1.59757) (0.00000) (0.00000) (0.00000) (0.00000) 2 0.008859 0.340954 98.73153 0.897763 0.003932 0.002990 0.022829 (1.84460) (2.33779) (1.47980) (0.44361) (0.42464) (0.65326) 3 0.011227 0.286133 96.36517 2.546289 0.074187 0.475224 0.252992 (2.18806) (4.48096) (2.99313) (1.02304) (1.09024) (1.88635) 4 0.013159 0.255367 93.04895 5.208158 0.766114 0.352682 0.368728 (2.33523) (6.33795) (4.34471) (2.36837) (1.29941) (2.94072) 5 0.014692 0.265925 89.99949 7.061169 2.082272 0.290888 0.300258 (2.57352) (7.86296) (5.37637) (3.75953) (1.42074) (3.43546) 6 0.015873 0.312893 87.00777 8.981647 3.185427 0.249901 0.262365 (2.51446) (9.13998) (6.28143) (4.79973) (1.49966) (3.58319) 7 0.017044 0.292469 85.13573 9.038898 4.827047 0.318047 0.387810 (2.54531) (9.85358) (6.63881) (5.86741) (1.63266) (3.36213) 8 0.017820 0.299374 84.04232 8.696062 5.988998 0.467279 0.505970 (2.70822) (10.4907) (6.88866) (6.95301) (1.83774) (3.42612) 9 0.018339 0.529600 81.88873 8.297288 8.084080 0.462150 0.738149 (2.97733) (11.3400) (6.96991) (8.19245) (1.99789) (3.68736) 10 0.018784 1.556269 78.39933 8.047879 10.38532 0.442164 1.169038 (3.41846) (12.1079) (7.02828) (9.16264) (2.08509) (4.22720)

Variance Decomposition of LMBASE: Perio... S.E. LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.037070 0.116458 3.303332 96.58021 0.000000 0.000000 0.000000 (0.72727) (2.17834) (2.32601) (0.00000) (0.00000) (0.00000) 2 0.049102 0.461471 1.890158 95.67028 1.325886 0.651602 0.000599 (1.44395) (1.67739) (3.37796) (1.49189) (1.36058) (0.89852) 3 0.057173 0.375530 1.634684 89.83101 5.189783 2.873262 0.095728 (1.75673) (2.09806) (6.06496) (3.85411) (3.02949) (1.32656) 4 0.064734 0.313461 2.156623 82.39973 11.69077 3.303029 0.136384 (2.47056) (2.70004) (8.62125) (6.35053) (3.57145) (1.75269) 5 0.070853 0.520616 2.496674 78.18960 14.99409 2.908568 0.890455 (2.99837) (3.21933) (9.47010) (7.64739) (3.45696) (2.02924) 6 0.077332 2.477749 4.821870 69.93601 18.16724 2.442298 2.154834 (3.40570) (4.55745) (10.4613) (8.27984) (3.10735) (2.90826) 7 0.082656 5.233792 5.475875 62.71093 21.81990 2.228416 2.531089 (4.46952) (5.47903) (10.9566) (8.91156) (3.13499) (3.22125) 8 0.091024 8.042411 7.904941 53.29468 26.36608 2.098459 2.293435 (5.17992) (6.53836) (11.1288) (9.00971) (3.14436) (3.28108) 9 0.098416 10.57308 10.41419 46.52366 27.96212 2.363812 2.163131 (6.08124) (7.34434) (11.1460) (8.83858) (3.41204) (3.70096) 10 0.107921 13.89233 14.65714 39.17353 27.44875 2.807144 2.021104 (7.06941) (8.08891) (10.7311) (8.53242) (3.78880) (4.09320)

224 Table (20): Variance Decomposition for the Reduced SVAR (Monetary Base (Model (B)) (Cont’d):

Variance Decomposition of LM2: Perio... S.E. LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.005467 1.359243 0.075856 0.024981 98.53992 0.000000 0.000000 (1.99946) (0.69312) (0.78997) (2.17364) (0.00000) (0.00000) 2 0.008456 6.076505 7.305339 0.153189 86.18179 0.048426 0.234752 (4.26849) (3.85791) (1.42294) (5.38306) (0.42330) (0.73718) 3 0.010559 4.656666 6.783222 0.485408 87.81462 0.072522 0.187558 (4.13274) (4.36597) (1.58665) (5.62394) (0.83433) (0.94912) 4 0.012186 5.977241 7.096576 0.623378 86.09717 0.054764 0.150875 (5.41963) (5.04769) (2.03490) (6.76341) (1.25131) (1.45128) 5 0.014025 6.929197 10.11841 0.522458 81.37995 0.319970 0.730016 (6.25690) (6.22595) (2.47045) (8.11489) (1.50118) (2.24436) 6 0.016013 6.540588 13.46210 1.053252 75.10261 0.381076 3.460371 (6.11463) (7.43983) (3.05020) (9.29217) (1.91979) (4.11566) 7 0.018062 6.167939 15.46067 0.942648 71.12190 0.749281 5.557564 (5.83170) (8.42131) (3.29391) (10.1520) (2.25558) (5.63634) 8 0.019455 6.296837 15.99683 0.822814 68.96957 1.150433 6.763517 (6.07641) (9.02614) (3.40922) (10.9391) (2.74619) (6.76170) 9 0.021188 6.279752 17.01902 0.759293 67.13578 1.624931 7.181227 (6.14737) (9.50464) (3.64429) (11.4876) (3.12376) (7.29797) 10 0.022915 6.501706 17.36093 0.807397 66.34713 1.865423 7.117418 (6.33382) (10.0318) (3.57972) (11.7857) (3.39393) (7.56171)

Variance Decomposition of LCRDTPRVTOT: Perio... S.E. LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.005479 1.234407 2.038153 2.927709 9.627837 84.17189 0.000000 (1.81784) (1.84175) (2.68860) (3.82443) (5.17712) (0.00000) 2 0.007053 1.929629 2.823285 4.507034 15.29277 75.38996 0.057321 (2.55416) (3.03598) (3.69796) (5.93354) (6.82569) (1.39153) 3 0.008601 1.324072 1.903921 5.776106 22.15645 68.80090 0.038547 (1.95064) (2.46846) (4.61248) (7.51633) (8.30799) (1.63227) 4 0.010100 1.582156 1.413159 8.066998 28.52565 59.24744 1.164601 (2.47619) (2.21264) (5.75398) (8.13461) (8.76664) (3.09212) 5 0.011867 1.837524 2.907547 7.921581 33.28102 49.63227 4.420055 (3.29342) (3.03195) (5.74948) (8.06386) (8.85619) (4.66064) 6 0.013674 1.714844 3.372699 8.334485 38.91993 41.21734 6.440705 (3.55080) (3.92951) (5.95027) (8.27377) (8.65735) (5.60882) 7 0.015429 2.504797 3.722576 7.022541 44.56079 34.81881 7.370486 (4.42992) (4.55773) (5.48559) (8.78048) (8.50656) (6.28483) 8 0.017143 3.336776 3.493314 6.145279 47.26072 29.61246 10.15146 (5.18760) (4.65787) (5.14325) (9.43596) (8.18905) (7.43457) 9 0.019047 5.783882 3.535048 5.125604 49.60559 25.00239 10.94749 (6.33404) (4.87834) (4.70616) (9.84627) (7.72184) (7.83375) 10 0.020815 7.705666 3.982269 4.402625 51.78040 21.41551 10.71352 (7.19445) (5.24391) (4.54529) (10.2880) (7.44609) (7.79942)

Variance Decomposition of LREER2000: Perio... S.E. LRGDP200... LCPI2000 LMBASE LM2 LCRDTPRV... LREER200...

1 0.020208 1.990686 7.835774 0.385884 3.990744 3.909485 81.88743 (2.52169) (3.50553) (1.31329) (2.71123) (2.48260) (5.55275) 2 0.033218 1.881373 11.39180 0.290254 10.74090 4.739606 70.95606 (2.81350) (5.47994) (1.65483) (4.64979) (3.07346) (6.96403) 3 0.040800 1.633022 11.30114 0.205506 10.69270 5.399481 70.76816 (3.28251) (6.47716) (1.89930) (5.69205) (3.97168) (8.60609) 4 0.046437 1.289006 10.33737 0.806323 9.178257 6.984443 71.40460 (3.14188) (6.75764) (2.29119) (5.69062) (5.18133) (9.44221) 5 0.051886 1.110473 9.331020 0.670100 8.303001 7.729591 72.85581 (3.02129) (6.76110) (2.56988) (5.76042) (5.49832) (9.94421) 6 0.057396 1.618820 8.211127 0.555689 7.648686 8.048311 73.91737 (3.50019) (6.74636) (2.78669) (5.85910) (5.53581) (10.2901) 7 0.061492 2.259438 8.028141 0.631630 6.848382 8.620707 73.61170 (4.41226) (7.07619) (3.21691) (5.87648) (5.67005) (10.8322) 8 0.063956 2.829703 7.425759 0.743726 6.330985 9.017151 73.65268 (5.22888) (6.60491) (3.64756) (5.74064) (5.87247) (11.0679) 9 0.066280 3.465792 7.104375 1.023894 5.937267 9.304412 73.16426 (5.85688) (6.03964) (3.95688) (5.77787) (6.04701) (11.2285) 10 0.068317 4.460491 6.857974 1.254750 5.597124 9.678141 72.15152 (6.44584) (5.73726) (4.39363) (5.82782) (6.13942) (11.2843)

Cholesky Ordering: LRGDP2000 LCPI2000 LMBASE LM2 LCRDTPRVTOT LREER2000 Standard Errors: Monte Carlo (100 repetitions)

225

Annex XIII: Granger Causality, Impulse Response and Variance

Decomposition (graphs): Monetary Base Model (Model (B))

226 Graph (24): Impulse Response Function Graphs- SVAR- Monetary Base Model (One Std. Dev.) 0 10 10 10 10 10 10 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 ResponseLREER2000LM2 to of 1 1 1 1 1 1 R esponsLR to EER 2000LMBASE of e ResponseLCPI2000of LREER2000 to ResponseLREER2000of LREER2000 to ResponseLRGDP2000 of LREER2000 to .15 .10 .05 .00 .04 .02 .00 .04 .02 .00 -.05 -.02 -.04 -.02 ResponseLCRDTPRVTOT of LREER200to .010 .005 .000 .010 .005 .000 .012 .008 .004 .000 -.005 -.010 -.005 -.010 -.004 -.008 10 10 10 10 10 10 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 ResponseLCRDTPRVTOTLM2 to of R esponseLCto R LMBASE D TPRof VTOT ResponseLCPI2000LCRDTPRVTOT of to .15 .10 .05 .00 .04 .02 .00 .04 .02 .00 -.05 -.02 -.04 -.02 ResponseLREER2000of LCRDTPRVTOT to ResponseLRGDP2000of LCRDTPRVTOT to .010 .005 .000 .010 .005 .000 .012 .008 .004 .000 -.005 -.010 -.005 -.010 -.004 -.008 ResponseLCRDTPRVTOT of LCRDTPRVTOT to 10 10 10 10 10 10 . 9 9 9 9 9 9 E . 8 8 8 8 8 8 S

2 7 7 7 7 7 7

6 6 6 6 6 6 s n 5 5 5 5 5 5 o i t 4 4 4 4 4 4 a v 3 3 3 3 3 3 o n ResponseLM2LM2 of to n 2 2 2 2 2 2 I R esponsLM2 to LMBASE of e

ResponseLCPI2000 of LM2 to . ResponseLREER2000 of LM2 to ResponseLRGDP2000 of LM2 to 1 1 1 1 1 1 D . ResponseLCRDTPRVTOT of LM2 to S

.15 .10 .05 .00 .04 .02 .00 .04 .02 .00 -.05 -.02 -.04 -.02 .010 .005 .000 .010 .005 .000 .012 .008 .004 .000 e -.005 -.010 -.005 -.010 -.004 -.008 n O

d e z i r 10 10 10 10 10 10 o t c 9 9 9 9 9 9 a f 8 8 8 8 8 8 n o 7 7 7 7 7 7 N

o 6 6 6 6 6 6 t

e 5 5 5 5 5 5 s n 4 4 4 4 4 4 o p 3 3 3 3 3 3 s e 2 2 2 2 2 2 R R es ponseLM2LMBASE ofto R esLMBASE ponse to LMBASE of 1 1 1 1 1 1 R esponseLCof LMBASE PI2000 to R esponseLRof EER 2000LMBASE to R esponsLRof GD e P2000LMBASE to R esponseLC of R DLMBASE TPR to VTOT .15 .10 .05 .00 .04 .02 .00 .04 .02 .00 -.05 -.02 -.04 -.02 .010 .005 .000 .010 .005 .000 .012 .008 .004 .000 -.005 -.010 -.005 -.010 -.004 -.008 10 10 10 10 10 10 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 ResponseLCPI2000LM2 ofto 1 1 1 1 1 1 R esponseLC to PI2000 LMBASE of ResponseLCPI2000 of LCPI2000 to ResponseLREER2000 of LCPI2000to ResponseLRGDP2000 of LCPI2000to ResponseLCRDTPRVTOTof LCPI2000 to .15 .10 .05 .00 .04 .02 .00 .04 .02 .00 -.05 -.02 -.04 -.02 .010 .005 .000 .010 .005 .000 .012 .008 .004 .000 -.005 -.010 -.005 -.010 -.004 -.008 10 10 10 10 10 10 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 ResponseLRGDP2000LM2 to of R esponseLR to GD LMBASE P2000 of ResponseLCPI2000 LRGDP2000of to ResponseLREER2000of LRGDP2000 to ResponseLRGDP2000of LRGDP2000 to .15 .10 .05 .00 .04 .02 .00 .04 .02 .00 -.05 -.02 -.04 -.02 .010 .005 .000 .010 .005 .000 .012 .008 .004 .000 ResponseLCRDTPRVTOT of LRGDP2000 to -.005 -.010 -.005 -.010 -.004 -.008

227 Graph (25): Variance Decomposition Graphs- SVAR- Monetary Base Model (One Std. Dev.) 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 Percent variance LM2 due to LRE ER2000 0 0 0 0 0 0 Percent LMBASE LREER2000to Percent due variance 80 40 80 40 80 40 80 40 80 40 80 40 -40 -40 -40 -40 -40 -40 PercentLCPI2000 variance to due LREER2000 120 120 120 120 120 120 PercentLREER2000 variance LREER2000 to due Percent LRGDP2000 variance to due LREER2000 Percent LCRDTPRVTOTvariance LREER2000 to due 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 0 0 0 0 0 0 80 40 80 40 80 40 Percent variance LM2 due LCRDTPRVTOT to 80 40 80 40 80 40 -40 -40 -40 -40 -40 -40 120 120 120 120 120 120 Percent LMBASEPercent LCRDTPRVTOT to due variance Percent LCPI2000 variance to due LCRDTPRVTOT Percent LREER2000 variance LCRDTPRVTOT to due Percent LRGDP2000 variance to due LCRDTPRVTOT Percent LCRDTPRVTOT variance to due LCRDTPRVTOT 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 . 3 3 3 3 3 3 E . 2 2 2 2 2 2 S Percent variance LM2 due LM2 to 1 1 1 1 1 1 2 Percent LMBASELM2 Percent to due variance

Percent LCPI2000variance due to LM2 Percent LREER2000 variance to due LM2 ± Percent LRGDP2000 variance to due LM2 0 0 0 0 0 0

Percent LCRDTPRVTOTvariance to due LM2 80 40 80 40 80 40 80 40 80 40 80 40 -40 -40 -40 -40 -40 -40 n 120 120 120 120 120 120 o i t i s o p m o c e D

e 0 c 10 10 10 10 10 1 n a i 9 9 9 9 9 9 r a 8 8 8 8 8 8 V 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 Percent LM2 variance due to LMBASE to due variance LM2 Percent Percent LMBASE variance due to LMBASEto due LMBASEvariance Percent Percent LCPI2000 variance due to LMBASEto due variance LCPI2000 Percent 0 0 0 0 0 0 80 40 80 40 80 40 80 40 80 40 LMBASE to LREER2000due Percent variance 80 40 Percent LRGDP2000 variance due to LMBASE to due LRGDP2000Percent variance -40 -40 -40 -40 -40 -40 120 120 120 120 120 120 Percent LCRDTPRVTOT variance due to LMBASE to due LCRDTPRVTOT variance Percent 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 Percentvariance LM2 due LCPI2000 to Percent LMBASEPercent LCPI2000 to due variance 0 0 0 0 0 0 Percent LCP variance I2000 to due LCPI2000 80 40 80 40 80 40 80 40 80 40 80 40 -40 -40 -40 -40 -40 -40 Percent LREER2000 variance due LCPI2000to Percent LRGDP2000 variance due to LCPI2000 120 120 120 120 120 120 Percent LCRDTPRVTOT variance to due LCPI2000 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 Percent variance LM2 due to LRGDP2000 0 0 0 0 0 0 80 40 80 40 80 40 80 40 80 40 80 40 Percent LMBASEPercent LRGDP2000to due variance -40 -40 -40 -40 -40 -40 120 Percent LCPI2000 variance due to LRGDP2000 120 120 120 120 120 Percent LREER2000 variance to due LRGDP2000 Percent LRGDP variance 2000 due to LRGDP2000 ercent ercent LCRDTPRVTOT variance to due LRGDP2000 P

228

Annex XIV: Diagnostic Tables for Recursive VAR Identification

Strategy (Cholesky Decomposition): Lending Rate Model (Model (R))

229 Table (21): Lending Rate Model (Model (R)) Lag Structure and Reduction Tests - June 1997 - June 2013:

Significance of Each Lag Joint AIC SC F Test significance of all lags Restricted Lags 17 16 15 14 13 12

0.96605 0.96385 0.68398 0.60251 1.1231 1.3349 0.96605 -27.71 -15.39 F(36,253 17 [0.5291] [0.5327] [0.9148] [0.9656] [0.2982] [0.1059] [0.5291]

1.1989 0.62871 0.80387 1.1345 1.3621 1.1989 -27.55 -15.88 F(36,279) 16 [0.2107] [0.9532] [0.7826] [0.2826] [0.0893] [0.2107]

0.62936 0.83764 0.95992 1.2747 0.62936 -27.42 -17.05 F(36,305) 15 [0.9534] [0.7347] [0.5387] [0.1426] [0.9534]

1.1249 0.98794 1.2598 1.1249 -27.42 -17.05 F(36,332) 14 [0.2920] [0.4928] [0.1528] [0.2920]

1.3875 1.4256 1.3875 -27.33 -17.60 F(36,358) 13 [0.0736] [0.0583] [0.0736]

1.6299 1.6299 -27.16 -18.09 F(36,384) 12 [0.0145]* [0.0145]*

P-values are in brackets ; *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

The Lending Rate Model’s VAR System is presented as follows:

Lrgdp2000 Lrgdp2000 , Lcpi2000 , Lcpi2000 Lending Rate = + ∑ , Lending Rate . + dumm201011+ Lm2 Lcrdtprvtot , Lcrdtprvtot Lreer2000 , Lreer2000 dumm201102 + dumm201112 +Centered dumm201007 Seasonal + Dummies dumm201004+ + dumm201005+ Where:

GDP, CPI, M2, Credit to Private Sector and Real Effective Exchange Rate are in Logs

dummXXXX: dummy variable (year/month)

: error term

230 Table (22): Residual Diagnostics Tests for the - 13 Lags VAR- Lending Rate Model (Model (R)):

231 Table (23): Structural VAR Estimates- 13 Lags SVAR- Lending Rate Model (Model (R)) (Recursive Identification):

Coefficient Std. Error z-Statistic Prob.

Log likelihood 3013.718

232

Annex XV: Graphical Representation and Diagnostics for Recursive

VAR Identification Strategy (Cholesky Decomposition): Lending Rate

Model (Model (R))

233 Graph (26): Plot of Log Levels and First Differences of Logs of Variables for- Lending Rate (Model (R)): 2010 2010 2010 2010 2010 2010 2005 2005 2005 2005 2005 2005 2000 2000 2000 2000 2000 2000 DLrgdp2000 DLrgdp2000 DLcpi2000 DLENDRT DLm2 DLcrdtprvtot DLreer2000 1 -1 0.5 0.0 0.1 -0.1 0.04 0.00 0.05 0.00 0.025 -0.025 2010 2010 2010 2010 2010 2010 2005 2005 2005 2005 2005 2005 2000 2000 2000 2000 2000 2000 Lrgdp2000 Lrgdp2000 Lcpi2000 LENDRT Lm2 Lcrdtprvtot Lreer2000 9 8 7 6 6 5 14 12 5.5 5.0 4.6 4.2

234 Graph (27): Diagnostic Graphs of - 13 Lags- VAR Model: Lending Rate (Model (R)): 10 10 10 10 10 10 5 5 5 5 5 5 ACF-r:Lrgdp2000 ACF-r:Lrgdp2000 ACF-r:Lcpi2000 ACF-r:LENDRT ACF-r:Lm2 ACF-r:Lcrdtprvtot ACF-r:Lreer2000 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 5.0 5.0 5.0 5.0 5 2.5 2.5 2.5 2.5 0 N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) N(0,1) 0.0 0.0 0.0 0.0 0 r:Lrgdp2000 r:Lrgdp2000 r:Lcpi2000 r:LENDRT r:Lm2 r:Lcrdtprvtot r:Lreer2000 -5 -2.5 Density Density Density Density Density Density -2.5 -2.5 -2.5 0.4 0.2 0.6 0.2 0.6 0.2 0.4 0.2 0.50 0.25 0.50 0.25 2010 2010 2010 2010 2010 2010 r:Lrgdp2000 (scaled) (scaled) r:Lrgdp2000 (scaled) r:Lcpi2000 (scaled) r:LENDRT (scaled) r:Lm2 (scaled) r:Lcrdtprvtot (scaled) r:Lreer2000 2000 2000 2000 2000 2000 2000 2 0 2 0 2 0 5 0 2 2 -2 -2 2010 2010 2010 2010 2010 2010 Fitted Fitted Fitted Fitted Fitted Fitted Lrgdp2000 Lrgdp2000 Lcpi2000 LENDRT Lm2 Lcrdtprvtot Lreer2000 2000 2000 2000 2000 2000 2000 7 6 6 5 14 12 9.5 8.5 5.5 5.0 4.6 4.2

235 Graph (28): Scaled Residuals - 13 Lags- VAR Model: Lending Rate (Model (R)):

0.50 r Lrgdp2000 r Lcpi2000 0.01 0.25

0.00 0.00

-0.25 -0.01

2000 2005 2010 2000 2005 2010 1 r LENDRT r Lm2 0.025

0 0.000

-0.025

2000 2005 2010 2000 2005 2010

0.025 r Lcrdtprvtot 0.1 r Lreer2000

0.000 0.0

-0.025 -0.1 2000 2005 2010 2000 2005 2010

236 Graph (29): Stability Test (One Step and N Down Chow Test) - 13 Lags- VAR Model: Lending Rate (Model (R)): 1% 1% 2010 2010 2010 1% 1up Lm2 Ndn Lrgdp2000 Ndn Lcrdtprvtot 2000 2000 2000 1.0 0.5 0.0 1.0 0.5 1.0 0.5 1% 2010 2010 2010 1% 1% 1up LENDRT 1up CHOWs Ndn Lm2 2000 2000 2000 1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 1% 1% 1% 2010 2010 2010 2010 1% 1up Lcpi2000 1up Lreer2000 Ndn Ndn LENDRT Ndn CHOWs 2000 2000 2000 2000 1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 1.0 0.5 1% 1% 1% 1% 2010 2010 2010 2010 1up Lrgdp2000 1up Lcrdtprvtot Ndn Ndn Lcpi2000 Ndn Lreer2000 2000 2000 2000 2000 1.5 1.0 0.5 0.0 1.5 1.0 0.5 0.0 1.0 0.5 1.0 0.5

237

Annex XVI: Structural VAR Estimates: Non - Recursive SVAR

Identification Strategy (Theory Based Restrictions): Lending Rate

Model (Model (R))

238 Table (24): Structural VAR Estimates- 13 Lags SVAR- Recursive SVAR Identification Strategy (Theory Based Restrictions): Lending Rate Model (Model (R))

Structural VAR Estimates Date: 10/10/15 Time: 12:15 Sample (adjusted): 1998M07 2013M06 Included observations: 180 after adjustments Estimation method: method of scoring (analytic derivatives) Convergence achieved after 1 iterations Structural VAR is just-identified

Model: Ae = Bu where E[uu']=I Restriction Type: short-run pattern matrix A = 1 0 0 0 0 0 C(1) 1 0 0 0 0 0 C(5) 1 C(11) 0 C(14) C(2) C(6) C(9) 1 0 0 C(3) C(7) C(10) C(12) 1 C(15) C(4) C(8) 0 C(13) 0 1 B = C(16) 0 0 0 0 0 0 C(17) 0 0 0 0 0 0 C(18) 0 0 0 0 0 0 C(19) 0 0 0 0 0 0 C(20) 0 0 0 0 0 0 C(21)

Coefficient Std. Error z-Statistic Prob.

C(1) 0.007784 0.003764 2.068045 0.0386 C(2) -0.655951 9.553167 -0.068663 0.9453 C(3) 0.010764 0.003874 2.778170 0.0055 C(4) 0.007488 0.018390 0.407158 0.6839 C(5) 32.83537 147.7058 0.222302 0.8241 C(6) 6.258109 90.78949 0.068930 0.9450 C(7) -0.087580 0.076534 -1.144337 0.2525 C(8) -0.906924 0.424401 -2.136952 0.0326 C(9) -1.911953 27.84039 -0.068676 0.9452 C(10) -0.001712 0.002030 -0.843182 0.3991 C(11) -263.0576 374.7682 -0.701921 0.4827 C(12) -0.354315 0.063567 -5.573896 0.0000 C(13) -1.385062 6.717568 -0.206185 0.8366 C(14) -52.21905 177.6676 -0.293914 0.7688 C(15) 0.077936 0.019501 3.996622 0.0001 C(16) 0.107552 0.005668 18.97367 0.0000 C(17) 0.005431 0.000286 18.97367 0.0000 C(18) 1.780523 3.201144 0.556215 0.5781 C(19) 0.383823 5.597697 0.068568 0.9453 C(20) 0.005434 0.000286 18.97367 0.0000 C(21) 0.026190 0.027470 0.953399 0.3404

Log likelihood 2633.704

Estimated A matrix: 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.007784 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 32.83537 1.000000 -263.0576 0.000000 -52.21905 -0.655951 6.258109 -1.911953 1.000000 0.000000 0.000000 0.010764 -0.087580 -0.001712 -0.354315 1.000000 0.077936 0.007488 -0.906924 0.000000 -1.385062 0.000000 1.000000 Estimated B matrix: 0.107552 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.005431 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.780523 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.383823 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.005434 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.026190

239

Annex XVII: Granger Causality, Impulse Response and Variance

Decomposition (tables): Lending Rate Model (Model (R))

240 Table (25): Granger Causality Tables- 13 Lags VAR- Lending Rate Model (Model (R)):

VAR Granger Causality/Block Exogeneity Wald Tests Date: 10/11/15 Time: 08:42 Sample: 1997M06 2013M06 Included observations: 180

Dependent variable: LRGDP2000

Excluded Chi-sq df Prob.

LCPI2000 11.22236 13 0.5922 LENDRT 13.98234 13 0.3751 LM2 19.94092 13 0.0967 LCRDTPRVT... 18.60897 13 0.1357 LREER2000 8.189777 13 0.8310

All 95.08689 65 0.0088

Dependent variable: LCPI2000

Excluded Chi-sq df Prob.

LRGDP2000 16.49460 13 0.2234 LENDRT 15.35941 13 0.2855 LM2 16.66838 13 0.2149 LCRDTPRVT... 28.31602 13 0.0082 LREER2000 18.01169 13 0.1571

All 90.20792 65 0.0210

Dependent variable: LENDRT

Excluded Chi-sq df Prob.

LRGDP2000 11.55606 13 0.5643 LCPI2000 12.15181 13 0.5152 LM2 14.52990 13 0.3376 LCRDTPRVT... 5.961372 13 0.9475 LREER2000 10.37036 13 0.6634

All 60.99373 65 0.6179

Dependent variable: LM2

Excluded Chi-sq df Prob.

LRGDP2000 31.59005 13 0.0028 LCPI2000 21.21626 13 0.0688 LENDRT 30.54475 13 0.0039 LCRDTPRVT... 8.395266 13 0.8169 LREER2000 7.561625 13 0.8709 All 95.20153 65 0.0087

241 Table (25): Granger Causality Tables- 13 Lags VAR- Lending Rate Model (Model (R)):

Dependent variable: LCRDTPRVTOT

Excluded Chi-sq df Prob.

LRGDP2000 18.65676 13 0.1341 LCPI2000 29.40427 13 0.0057 LMBASE 9.146129 13 0.7618 LM2 32.14003 13 0.0023 LREER2000 36.61552 13 0.0005

All 130.5037 65 0.0000

Dependent variable: LREER2000

Excluded Chi-sq df Prob.

LRGDP2000 12.24258 13 0.5079 LCPI2000 26.13514 13 0.0163 LMBASE 14.19769 13 0.3601 LM2 26.39281 13 0.0150 LCRDTPRVT... 16.49201 13 0.2236

All 95.04003 65 0.0089

242 Table (26): Impulse Response Function Tables- 13 Lags VAR- Lending Rate Model (Model (R)): (Non Factorized One SD Deviation)

Response of LRGDP2000: Perio... LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.107552 0.000000 0.000000 0.000000 0.000000 0.000000 2 0.000462 0.007522 0.001491 0.025532 -0.002385 -0.003051 3 0.000960 -0.021611 -0.001737 -0.001072 -0.033965 0.005728 4 0.037240 0.006862 0.005297 -0.018005 0.007059 -0.013870 5 0.020533 0.001363 -0.009879 0.000521 -0.004364 0.002307 6 0.017690 0.007129 -0.023325 -0.000737 -0.013882 0.004986 7 0.022843 0.014983 -0.033898 0.003680 -0.005769 0.002804 8 0.020619 -0.000264 -0.002467 -0.009188 -0.006404 0.008556 9 0.033039 -0.000860 -0.023651 -0.014913 -0.002455 -0.001303 10 0.019526 0.019004 -0.019659 -0.027033 -0.008140 -0.017025

Response of LCPI2000: Perio... LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.000000 0.005495 0.000000 0.000000 0.000000 0.000000 2 -0.000778 0.007151 -0.000753 0.000507 -0.000204 0.000141 3 -0.001368 0.007052 -0.001094 0.001895 -0.000560 0.001381 4 -0.001219 0.006758 -0.001540 0.003132 -0.001841 0.001931 5 -0.001368 0.007119 -0.002178 0.003344 -0.001776 0.001378 6 -0.000973 0.007032 -0.002537 0.003163 -0.001667 0.001026 7 -0.001561 0.007525 -0.002494 0.002911 -0.001980 0.000418 8 -0.002155 0.006825 -0.002987 0.002042 -0.001758 0.000871 9 -0.002097 0.005373 -0.004292 0.002486 -0.001172 0.001152 10 -0.000625 0.003707 -0.004481 0.002088 -0.000419 0.001507

Response of LENDRT: Perio... LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.000000 0.000000 0.205720 0.000000 0.000000 0.000000 2 -0.039641 -0.013047 0.157464 0.040149 0.017335 0.009342 3 -0.013793 -0.008606 0.134625 0.003305 -0.000462 -0.019829 4 -0.018950 0.005683 0.160942 0.013158 0.027250 -0.005552 5 -0.038044 -0.018359 0.144880 0.039382 0.037403 -0.015458 6 -0.034617 -0.011743 0.159035 0.079467 0.023207 -0.027633 7 -0.040683 -0.015200 0.127772 0.077408 0.035292 0.009726 8 -0.022209 0.034832 0.121575 0.044270 0.051518 -0.003459 9 -0.029616 -0.004790 0.110491 0.087409 0.049415 0.002860 10 -0.029841 -0.009088 0.110711 0.091874 0.040214 0.031829

Response of LM2: Perio... LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.000000 0.000000 0.000000 0.006734 0.000000 0.000000 2 0.000910 -0.001183 -0.000136 0.006395 0.000577 0.000582 3 -0.000892 -0.000586 -0.000305 0.006432 0.000161 0.000195 4 0.000403 -0.001110 0.000663 0.005250 0.000170 -0.000234 5 0.000540 -0.002567 0.001960 0.004501 0.001451 -0.000264 6 6.90E-05 -0.002949 0.001089 0.004879 -9.89E-05 -0.001261 7 0.000583 -0.003139 0.001562 0.004699 0.000754 -0.000632 8 0.000336 -0.002336 0.003147 0.004150 0.001494 -0.000420 9 0.000532 -0.003372 0.003320 0.005301 0.001491 -0.001449 10 0.001411 -0.003755 0.005419 0.006162 0.001177 -0.001010

Response of LCRDTPRVTOT: Perio... LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.000000 0.000000 0.000000 0.000000 0.006494 0.000000 2 -0.000886 0.000298 0.000311 0.002373 0.003649 0.000298 3 -0.001612 0.000690 0.000818 0.002537 0.005315 0.000413 4 -0.001246 -6.09E-05 0.001771 0.003713 0.004167 0.001764 5 -0.000756 -0.002468 0.002418 0.004615 0.005001 0.003512 6 -0.000629 -0.002896 0.001616 0.006366 0.003930 0.004267 7 0.000152 -0.002565 0.001482 0.006812 0.003474 0.004549 8 0.000672 -0.002642 0.002158 0.008155 0.003457 0.006358 9 0.002484 -0.003200 0.002733 0.008460 0.002511 0.005663 10 0.002842 -0.002999 0.002612 0.009420 0.001579 0.005792

Response of LREER2000: Perio... LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.000000 0.000000 0.000000 0.000000 0.000000 0.022375 2 -0.000244 0.001917 -0.000472 -0.004676 -0.004055 0.022864 3 0.001026 0.002136 -0.002377 0.001331 -0.006650 0.019899 4 0.005677 0.001792 -0.004229 0.007217 -0.009255 0.020969 5 0.005844 0.004208 -0.007522 0.005953 -0.007874 0.021686 6 0.008009 0.003624 -0.007697 0.006411 -0.008524 0.019567 7 0.008800 0.006508 -0.005517 0.006876 -0.008225 0.013176 8 0.007400 0.002297 -0.008152 0.006006 -0.006213 0.012377 9 0.006992 0.000565 -0.011114 0.003920 -0.007225 0.013204 10 0.008745 -0.000814 -0.011708 0.002826 -0.005040 0.013075 Nonfactorized One Std.Dev.

243 Table (27): Variance Decomposition for the Reduced SVAR (Lending Rate (Model (R) - Cholesky Decomposition):

Variance Decomposition of LRGDP2000: Perio... S.E. LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.107552 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 (0.00000) (0.00000) (0.00000) (0.00000) (0.00000) (0.00000) 2 0.110753 94.30246 0.313844 0.122515 5.187884 0.013222 0.060076 (5.04231) (1.47295) (1.62135) (4.35632) (1.34761) (1.45105) 3 0.118904 82.56586 3.298941 0.247383 6.767609 6.884442 0.235765 (6.77760) (3.54155) (1.77497) (4.65462) (4.23332) (1.90062) 4 0.125464 81.81672 3.116285 0.296028 6.793647 6.798323 1.178999 (6.77313) (3.19893) (1.70189) (4.34870) (4.35007) (2.18076) 5 0.128027 81.76288 2.997899 0.881280 6.551729 6.648227 1.157980 (7.02284) (3.32062) (2.63421) (4.24490) (4.52586) (2.32312) 6 0.133446 78.29232 2.957301 4.013301 6.426969 7.133785 1.176322 (7.03354) (3.37324) (3.36709) (4.30585) (4.54989) (2.63085) 7 0.140992 74.06818 3.411698 9.118699 5.757716 6.558636 1.085068 (7.27475) (3.17104) (4.57250) (4.24416) (4.14478) (2.53400) 8 0.143854 73.41486 3.287967 8.823828 6.552359 6.598676 1.322306 (7.11753) (3.25550) (4.18435) (4.24009) (4.19486) (2.86231) 9 0.151743 72.30277 2.988555 10.63417 6.935815 5.944452 1.194230 (7.48452) (3.23685) (5.20162) (4.36606) (3.94014) (3.18977) 10 0.158664 68.20612 3.531417 11.88600 8.908986 5.463909 2.003576 (7.93334) (3.34978) (5.81437) (4.80182) (3.89268) (3.48039)

Variance Decomposition of LCPI2000: Perio... S.E. LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.005495 2.320862 97.67914 0.000000 0.000000 0.000000 0.000000 (1.89265) (1.89265) (0.00000) (0.00000) (0.00000) (0.00000) 2 0.009109 4.296224 94.88106 0.582088 0.165874 0.055893 0.018860 (3.30927) (3.49454) (1.10200) (0.71817) (0.53732) (0.56903) 3 0.011952 5.920240 90.45810 0.868992 1.141735 0.543565 1.067368 (4.77441) (5.52878) (2.10001) (1.70049) (1.13767) (2.01619) 4 0.014306 5.596857 86.01807 1.387085 2.237357 2.573133 2.187497 (5.21236) (6.60254) (3.11505) (2.92647) (2.72313) (3.46588) 5 0.016444 5.414670 83.35736 2.353894 3.357332 3.305196 2.211549 (5.47486) (7.51650) (4.31884) (4.19852) (3.65263) (3.75448) 6 0.018226 4.920744 81.94517 3.439634 4.054413 3.589018 2.051026 (5.58313) (8.28626) (5.63161) (5.54796) (4.42601) (3.83456) 7 0.019998 4.971854 81.04885 4.156702 4.257656 3.826841 1.738101 (5.96230) (8.79024) (6.52882) (6.46540) (4.99542) (3.82718) 8 0.021485 5.502970 79.64387 5.325825 3.899939 3.991379 1.636022 (6.57160) (9.18177) (7.53855) (6.78395) (5.45843) (4.15745) 9 0.022643 5.688239 76.77898 7.903664 4.013771 3.937580 1.677770 (7.04683) (9.36118) (8.97079) (7.30398) (5.62214) (4.66022) 10 0.023386 5.353373 74.25573 10.54994 4.141511 3.798147 1.901302 (7.16605) (9.65099) (10.2749) (7.89075) (5.70455) (5.29360)

Variance Decomposition of LENDRT: Perio... S.E. LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.205720 3.708477 0.738767 95.55276 0.000000 0.000000 0.000000 (3.13149) (1.55537) (3.42003) (0.00000) (0.00000) (0.00000) 2 0.274317 8.871325 0.419360 87.73307 2.671484 0.212969 0.091794 (4.95731) (1.43057) (5.85976) (2.84138) (1.01803) (0.90631) 3 0.307030 8.531505 0.336211 88.31544 2.218459 0.194975 0.403409 (4.82255) (1.82434) (6.27645) (2.91648) (1.54662) (1.22845) 4 0.353413 8.882544 0.520624 87.36221 2.252474 0.658140 0.324004 (5.19409) (2.30328) (7.37977) (3.52707) (2.52513) (1.66096) 5 0.396130 10.06646 0.472439 83.57901 4.118979 1.384688 0.378422 (6.02649) (2.79413) (9.02918) (5.14510) (3.19798) (2.06516) 6 0.446439 10.06174 0.387613 79.45800 8.019503 1.471982 0.601158 (6.54286) (2.94640) (10.2957) (6.56773) (3.43435) (2.43349) 7 0.481328 10.73873 0.339326 76.38597 10.37604 1.610447 0.549480 (7.17020) (2.98541) (11.3485) (7.55327) (3.94945) (2.84266) 8 0.510087 10.92992 1.031566 74.28704 11.01499 2.243577 0.492905 (7.69994) (3.21287) (12.0892) (7.96437) (4.56179) (3.18668) 9 0.540264 10.92213 0.923933 71.27952 13.81736 2.615468 0.441597 (7.97309) (3.41358) (12.9661) (8.89984) (4.79960) (3.47213) 10 0.567249 10.97284 0.846423 69.31271 15.62458 2.593690 0.649766 (8.13687) (3.56925) (13.4594) (9.59684) (5.00280) (3.99405)

244 Table (27): Variance Decomposition for the Reduced SVAR (Lending Rate (Model (R) - Cholesky Decomposition)(Cont’d):

Variance Decomposition of LM2: Perio... S.E. LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.006734 0.004176 0.073424 1.131538 98.79086 0.000000 0.000000 (0.73748) (0.72468) (1.83474) (2.01910) (0.00000) (0.00000) 2 0.009493 1.146284 1.705563 0.988005 95.72743 0.135650 0.297068 (2.06007) (1.85762) (2.05005) (3.69791) (1.16032) (1.13279) 3 0.011509 1.198628 1.570830 0.796377 96.11109 0.098267 0.224804 (1.84260) (1.83554) (2.48117) (4.05600) (1.49244) (1.58083) 4 0.012826 1.095534 2.182764 1.535868 94.87244 0.106035 0.207361 (2.10779) (2.47491) (3.67634) (4.98931) (2.12999) (1.96809) 5 0.014359 0.930142 4.843176 4.274807 88.80815 0.951468 0.192252 (2.61918) (3.47678) (5.73107) (7.39533) (3.13151) (2.22618) 6 0.015724 0.854908 8.226680 4.517090 84.91361 0.818253 0.669457 (2.99857) (4.92876) (6.79185) (8.81212) (3.47060) (2.80530) 7 0.017040 0.895536 10.57223 5.320729 81.60093 0.931527 0.679054 (3.52954) (6.09215) (7.49100) (10.0712) (4.19262) (3.07455) 8 0.018297 0.781542 10.64226 8.548920 77.99042 1.406236 0.630626 (3.69604) (6.53320) (8.72310) (10.8195) (5.00567) (3.31407) 9 0.020176 0.660735 11.71985 10.74377 74.09400 1.854584 0.927061 (3.92102) (7.08166) (9.26923) (11.3255) (5.42833) (3.49350) 10 0.022535 0.667381 11.95983 15.74566 68.90819 1.816917 0.902016 (3.90703) (7.26976) (10.5936) (11.4165) (5.39871) (3.68728)

Variance Decomposition of LCRDTPRVTOT: Perio... S.E. LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.006494 3.224613 0.014092 0.697656 19.83367 76.22997 0.000000 (2.64583) (0.69540) (1.51862) (5.28947) (5.78105) (0.00000) 2 0.008408 5.782471 0.194025 1.488315 33.29643 59.13928 0.099476 (3.71237) (1.47354) (2.46930) (7.17410) (7.75480) (1.04742) 3 0.011188 9.713825 0.663581 2.703355 36.94916 49.80603 0.164050 (5.18821) (2.50616) (3.17515) (7.75847) (8.17124) (1.40705) 4 0.013240 10.27867 0.569285 5.583265 40.75360 41.29390 1.521273 (5.83445) (2.98063) (5.30521) (9.08768) (8.70637) (2.77866) 5 0.015742 8.805420 1.389259 8.572443 42.24216 33.97545 5.015268 (5.75623) (2.55192) (6.81408) (9.78092) (8.35718) (4.92659) 6 0.018087 7.313088 2.224945 8.756798 46.14805 27.35394 8.203180 (5.49484) (2.50657) (7.86163) (10.9880) (8.14256) (6.69084) 7 0.020126 6.003912 2.430165 8.751985 49.22843 22.91723 10.66827 (5.10521) (2.67889) (8.29903) (11.4828) (7.77851) (8.03637) 8 0.022641 4.766234 2.238682 9.263010 50.60634 18.45540 14.67034 (4.67711) (2.99039) (8.86291) (11.7290) (7.26862) (9.22999) 9 0.024943 4.374324 2.452181 10.15497 51.56750 15.28348 16.16754 (4.21618) (3.50907) (9.57393) (11.8494) (6.83488) (9.87645) 10 0.027223 4.315629 2.479622 10.54755 52.66835 12.83344 17.15541 (4.28479) (3.81053) (10.0752) (12.0128) (6.37269) (10.4972)

Variance Decomposition of LREER2000: Perio... S.E. LRGDP200... LCPI2000 LENDRT LM2 LCRDTPRV... LREER200...

1 0.022375 0.452000 4.361793 0.101814 8.914854 7.023339 79.14620 (1.30263) (3.15030) (0.71138) (3.97257) (3.29862) (5.60764) 2 0.035070 0.318166 5.433605 0.067659 17.96994 10.35236 65.85827 (1.57137) (3.48048) (1.32785) (6.15695) (4.38877) (7.59529) 3 0.042004 0.281173 5.792668 0.297755 15.78272 14.17428 63.67141 (1.58560) (4.09587) (2.41077) (6.88185) (6.16610) (9.21398) 4 0.048920 1.982164 5.525900 0.722797 12.06601 18.22109 61.48204 (2.71663) (4.54507) (3.16127) (6.20275) (7.75882) (10.4525) 5 0.055594 2.963815 6.240612 2.005821 9.877362 19.26336 59.64903 (3.89053) (5.36252) (4.14300) (5.88055) (8.64403) (11.3550) 6 0.061170 4.707835 6.365877 2.939944 8.443861 20.17320 57.36929 (5.07372) (5.49115) (5.41526) (5.63922) (9.25015) (12.2025) 7 0.064647 6.377432 7.397941 3.213217 7.573791 20.78680 54.65082 (5.98679) (6.18063) (6.09686) (5.52436) (9.86691) (12.6875) 8 0.067286 7.653163 7.168066 4.193240 6.996783 20.86216 53.12659 (6.65051) (6.37284) (7.09368) (5.35620) (10.2112) (12.9605) 9 0.070568 8.762952 6.611337 6.081185 6.575081 20.89833 51.07112 (7.09866) (6.38940) (8.07214) (5.37256) (10.3596) (13.1280) 10 0.073741 10.31870 6.065868 7.854355 6.226496 20.27612 49.25846 (7.53619) (6.32117) (8.71303) (5.39495) (10.1867) (13.0535)

Cholesky Ordering: LRGDP2000 LCPI2000 LENDRT LM2 LCRDTPRVTOT LREER2000 Standard Errors: Monte Carlo (100 repetitions)

245

Annex XVIII: Granger Causality, Impulse Response and Variance

Decomposition (graphs): Lending Rate Model (Model (R))

246 Graph (30): Impulse Response Function Graphs- SVAR- Lending Rate Model (Model (R)) (One Std. Dev.) 0 10 10 10 10 10 10 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 ResponseLREER2000LM2 to of 1 1 1 1 1 1 ResponseLENDRTof LREER2000 to ResponseLCPI2000of LREER2000to ResponseLREER2000of LREER2000to ResponseLRGDP2000of LREER2000to .1 .0 .3 .2 .1 .0 -.1 -.1 -.2 .01 .00 .01 .00 .04 .02 .00 -.01 -.01 -.02 -.04 ResponseLCRDTPRVTOTof LREER200 to .012 .008 .004 .000 -.004 -.008 10 10 10 10 10 10 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 ResponseLCRDTPRVTOTLM2to of ResponseLENDRTof LCRDTPRVTOT to .1 .0 .3 .2 .1 .0 ResponseLCPI2000LCRDTPRVTOT of to -.1 -.1 -.2 .01 .00 .01 .00 .04 .02 .00 -.01 -.01 -.02 -.04 ResponseLREER2000 of LCRDTPRVTOT to ResponseLRGDP2000of LCRDTPRVTOT to .012 .008 .004 .000 -.004 -.008 ResponseLCRDTPRVTOT of LCRDTPRVTOT to 10 10 10 10 10 10 9 9 9 9 9 9 . 8 8 8 8 8 8 E . 7 7 7 7 7 7 S

2 6 6 6 6 6 6 ±

5 5 5 5 5 5 s n 4 4 4 4 4 4 o i t 3 3 3 3 3 3 a v ResponseLM2LM2of to o 2 2 2 2 2 2 n ResponseLENDRTof LM2 to ResponseLCPI2000of LM2 to n ResponseLREER2000of LM2 to ResponseLRGDP2000of LM2to 1 1 1 1 1 1 I

ResponseLCRDTPRVTOTof LM2to . D .1 .0 .3 .2 .1 .0 -.1 -.1 -.2 . .01 .00 .01 .00 .04 .02 .00 -.01 -.01 -.02 -.04 .012 .008 .004 .000 -.004 -.008 S

e n O

y k s 10 10 10 10 10 10 e l 9 9 9 9 9 9 o h 8 8 8 8 8 8 C

7 7 7 7 7 7 o t

e 6 6 6 6 6 6 s n 5 5 5 5 5 5 o p 4 4 4 4 4 4 s e 3 3 3 3 3 3 R 2 2 2 2 2 2 ResponseLENDRTLM2of to 1 1 1 1 1 1 ResponseLENDRT of LENDRT to ResponseLCPI2000of LENDRT to ResponseLREER2000of LENDRT to ResponseLRGDP2000 of LENDRT to ResponseLCRDTPRVTOTof LENDRTto .1 .0 .3 .2 .1 .0 -.1 -.1 -.2 .01 .00 .01 .00 .04 .02 .00 -.01 -.01 -.02 -.04 .012 .008 .004 .000 -.004 -.008 10 10 10 10 10 10 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 ResponseLCPI2000LM2to of 1 1 1 1 1 1 ResponseLENDRTof LCPI2000to ResponseLCPI2000of LCPI2000 to ResponseLREER2000 of LCPI2000to ResponseLRGDP2000of LCPI2000 to .1 .0 .3 .2 .1 .0 ResponseLCRDTPRVTOTof LCPI2000to -.1 -.1 -.2 .01 .00 .01 .00 .04 .02 .00 -.01 -.01 -.02 -.04 .012 .008 .004 .000 -.004 -.008 10 10 10 10 10 10 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 ResponseLRGDP2000LM2to of 1 1 1 ResponseLENDRTof LRGDP2000 to ResponseLCPI2000 LRGDP2000of to ResponseLREER2000 of LRGDP2000 to .1 .0 .3 .2 .1 .0 ResponseLRGDP2000of LRGDP2000 to -.1 -.1 -.2 .01 .00 .01 .00 .04 .02 .00 -.01 -.01 -.02 -.04 .012 .008 .004 .000 ResponseLCRDTPRVTOT of LRGDP2000to -.004 -.008

247 Graph (31): Variance Decomposition Graphs- SVAR- Lending Rate Model (Model (R)) (One Std. Dev.) 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 Percent variance due LM2 to LRE ER2000 0 0 0 0 0 0 80 40 80 40 80 40 80 40 80 40 80 40 Percent LENDRT variance to LREER2000 due -40 -40 -40 -40 -40 -40 Percent LCPI2000 variance to due LREER2000 120 120 120 120 120 120 Percent LREER2000 variance to LREE due R2000 Percent LRGDP2000 variance to due LREER2000 Percent LCRDTPRVTOT variance to LREER2000 due 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 0 0 0 0 0 0 Percent LM2 Percentvariance LM2 to due LCRDTPRVTOT 80 40 80 40 80 40 80 40 80 40 80 40 -40 -40 -40 -40 -40 -40 120 120 120 120 120 120 Percent LENDRT variance due to LCRDTPRVTOT Percent LCPI2000 variance to due LCRDTPRVTOT Percent LREER2000 variance due to LCRDTPRVTOT Percent LRGDP2000 variance due to LCRDTPRVTOT Percent LCRDTPRVTOT variance to due LCRDTPRVTOT 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 . 3 3 3 3 3 3 E . 2 2 2 2 2 2 S

Percent variance LM2 due to LM2 1 1 1 1 1 1 2 Percent LENDRT variance due to LM2

Percent LCPI2000 variance due to LM2 Percent LREER2000 variance due to LM2 ± Percent LRGDP2000 variance to due LM2 0 0 0 0 0 0

Percent LCRDTPRV variance to due LM2 TOT 80 40 80 40 80 40 80 40 80 40 80 40 -40 -40 -40 -40 -40 -40 n 120 120 120 120 120 120 o i t i s o p m o c e D

e 0 c 10 10 10 10 10 1 n a i 9 9 9 9 9 9 r a 8 8 8 8 8 8 V 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 Percent variance LM2 to LENDRT due Percent LENDRT variance to LENDRT due 0 0 0 0 0 0 Percent LCPI2000 variance to due LE NDRT 80 40 80 40 80 40 80 40 80 40 80 40 Percent LREER2000 variance to LENDRT due Percent LRGDP2000 variance to LENDRT due -40 -40 -40 -40 -40 -40 120 120 120 120 120 120 Percent LCRDTPRVTOT to LENDRTvariance due 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 Percent variance LM2 due to LCPI2000 0 0 0 0 0 0 Percent LENDRT variance to LCPI2000due Percent LCPI2000 variance due to LCPI2000 80 40 80 40 80 40 80 40 80 40 80 40 -40 -40 -40 -40 -40 -40 Percent LREER2000 variance due to LCPI2000 Percent LRGDP2000 variance due to LCPI2000 120 120 120 120 120 120 Percent LCRDTPRVTOT variance due to LCPI2000 0 10 10 10 10 10 1 9 9 9 9 9 9 8 8 8 8 8 8 7 7 7 7 7 7 6 6 6 6 6 6 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 Percent variance LM2 due to LRGDP2000 0 0 0 0 0 0 80 40 80 40 80 40 80 40 80 40 80 40 -40 -40 Percent LENDRT variance to LRGDP2000 due -40 -40 -40 -40 Percent LCPI2000 variance due to LRGDP2000 120 120 120 120 120 120 Percent LREER2000 variance to due LRGDP2000 Percent LRGDP2000 variance due to LRGDP2000 ercent LCRDTPRVercent variance to LRGDP2000 due TOT P

248 Chapter III: Savings Behavior in Egypt

Abstract

Private sector savings play a pivotal role in financing development and growth

sustainment. Recently, there were many theoretical developments that underpin key

determinants of savings’ behavior; many of which merit empirical investigation. The

Egyptian government adopted an economic reform program in the 1990s. This followed a

full liberalization of determination of interest rates, leaving it to markets to determine the

opportunity cost of money holdings. The purpose of this chapter is to investigate private

savings behavior in Egypt, using quarterly data covering the years 1991–2010 and

adopting a Vector Equilibrium Correction Model (VECM). The key finding is that

Egypt’s private savings follow the Life Cycle Model proposed by Modigliani 1954,

especially in the long term. Controlling for population growth; we find that the real

interest rate and financial development are key determinants for real private savings in

the long run. However, in the short run, economic uncertainty proxied by inflation and

devaluation of the exchange rate are key determinants to private savings decisions in the

short term. Robust macroeconomic and monetary policies are pre-requisites for

sustainable growth in Egypt.

JEL codes: D91, D81, E21

Keywords: Time-Series Models, Savings, Uncertainty, Life cycle model, Permanent Income Hypothesis

249 1. Introduction

Understanding savings behavior is critical when designing economic policies to promote

investment and growth. The differences in patterns of economic growth in developing

and industrial countries reflect substantially on the behavior of economics agents. Most

of the empirical literature that analyzed cross-country saving behavior concentrated on

aggregate savings, due to the lack of consistent information on household behavior, and possible differences in the household savings in developing versus industrial countries

were disregarded (Loayza et. al. 2000).

Saving is alternatively defined as income minus consumption, or the change in wealth, or

the supply of capital. Given comprehensive and consistent definitions of each of these

terms, each definition of saving would represent the same concept and give rise to similar

empirical measures (Gale and Sabelhaus 1999).

The decline in saving will reflect on the state of economic growth and development. One

interpretation is that the low saving rate will spread the belief behaviorally that saving

will stay low. Low saving rates will lead to low capital accumulation. On the macro level,

a low saving rate triggers vulnerability of dependence on foreign capital; at a micro level,

it raises households’ fears of securing future consumption through pension systems for

retirement or other purposes.

Another interpretation is that sustaining short-term aggregate demand sees significant

good news in the low saving rate and concerns that saving will rebound. The decline in personal saving and the accompanying rise in consumption have helped fuel the

250 economic expansion in the US and helped prop up the global economy (Gale and

Sabelhaus 1999).

Nordhaus (1996) proposed a third view mentioning that current tools for measuring

saving and investment are stone-age definitions in the information age. His view

emphasized that measures of aggregate saving correspond weakly to the concepts of

saving. Accordingly, empirical analysis of different economic issues may require

examining different measures of saving. Despite the fact that he supports the view that

low saving or higher consumption props up the economy, the low quality of saving data

makes it difficult to reach this conclusion based on official saving aggregates alone.

Studying saving behavior in developing countries separately from developed economies

is motivated by four key principles (Deaton 1991):

• At the microeconomic level, developing-country households tend to be large and

poor; they have a different demographic structure; more are likely to be engaged

in agriculture; and their income prospects are much more uncertain. The problem

of allocating income over time looks rather different in the two contexts, and the

same basic models have different implications for behavior and policy.

• At the macroeconomic level, both developing and developed countries are

concerned with saving and growth, with the possible distortion of aggregate

saving, and with saving as a measure of economic performance. But few

developing countries possess the sort of fiscal system that permits deliberate

manipulation of personal disposable income to help stabilize output and

employment.

251 • Much of the postwar literature expresses the belief that saving is too low, and that

development and growth are impeded by the shortfall. Sometimes the problem is

blamed on the lack of government policy, sometimes on misguided policy.

• Saving is even more difficult to measure in developing rather than in advanced

economies, whether at the household level or as a macroeconomic aggregate. The

resulting data inadequacies are pervasive and have seriously hampered progress in

answering basic questions.

While an analysis of savings is significant to guide development policies in developing economies, this is challenged by the quality of data and strength of the analytical framework and interpretation of empirical outcomes. This chapter of the dissertation will focus on studying saving behavior in Egypt. The next section provides a literature review of recent empirical literature on saving behavior in addition to earlier research on modeling saving behavior in Egypt. This is followed by a section on the theoretical motivation followed by a section describing the data and the empirical methodology. The last two sections include the analysis of the empirical findings and the conclusion.

2. Recent Literature Review on Modeling Saving Behavior i. Recent Developments in Literature on Saving Behavior in Emerging and Developing Countries

The discussion on empirically investigating the behavior of savings and identifying key determinants has been visited recently in the literature. (Kwakwa 2013) investigated the determinants of national savings by employing the Johansen co-integration technique and error correction model to examine the short- and long-run dynamics of the system using time-series data for Ghana over the period 1975–2008. The study found all the variables

252 to be integrated of order one and the existence of co-integration indicated a valid long run

economic relationship among the determinants of national saving in Ghana. The

empirical results established that in the long run, income and terms of trade have a positive and significant impact on saving while the dependency ratio, political instability and the real interest rate have a negative impact on savings. In the short run however, only terms of trade affect savings positively. The other variables, namely dependency ratio, political instability, financial deepening, income, and interest rate, have an insignificant impact on saving.

The main takeaways from the findings of a survey of the recent literature are that in line with the classical theory, growth in income per capita has a predictive power to saving behavior in line with the Keynesian proposition. However, determinants of private savings behavior differ across countries, which merits individual country analyses to model saving behavior robustly and identify its key determinants. This is a key motivation of my deliberation in this chapter.

(Oskooee et al. 2012) introduce the “saving-investment” gap, which measures if the need for financing is unmet, as another determinant of income inequality. They apply time- series techniques in a study of 16 countries using the “bounds testing” co-integration methodology. They find varying results across countries due to the heterogeneous effect of determinants of saving in each country. They proposed further country-specific research to ensure robustness.

Awan et al. (2010) analyze the long run and short run associations among the real rate of interest on deposits, financial liberalization, economic growth, terms of trade, real

253 remittances by Pakistani emigrants, and domestic savings behavior in Pakistan, using

annual time series data for the period 1973–2007. An “Auto Regressive Distributed Lag

Bounds Testing” approach has been applied for a co-integration analysis. The results

reveal that the real interest rate, financial liberalization, and economic growth positively

affect domestic savings in Pakistan in the long run. The coefficient of the liberalization

dummy is also positive and statistically significant, which suggests a need for increased

liberalization and deregulation of the interest rate for mobilization of saving. Conversely,

the terms of trade and real remittances by Pakistani emigrants show a negative

relationship with domestic saving, supporting Complementarity Hypothesis of

(Mckinnon-Shaw 1973).

Dhanasekaran (2010) attempted to examine the causality between GDP and saving during

the period 1950-195151 to 2002-2003 in India using annual data. He adopts co-

integration and causality models that imply the presence of a stable long-run relationship between both variables. Having verified that GDP and saving are co-integrated, the

Granger test indicates the absence of any causal relationship between both variables.

However, a negative coefficient of the error correction term in the regression equation of

saving on GDP is observed. This negative coefficient signifies that saving converges to

its long-run equilibrium level.

Kim (2010) studies internal and external determinants that could affect personal saving in

the US using the time series data for the US between 1950 and 2007. The findings reveal

that personal saving is highly dependent on personal income, taxes, bank credit, and

employment, while dependency ratio, current real estate loan, real interest rate, and status

of economic performance are indeterminate.

254 Agrawal and Sahoo (2009) estimate the long-run total and private savings functions for

Bangladesh during 1975–2004 using annual data. They find that the total saving rate is

mainly determined by the GDP growth rate, dependency ratio, interest rates, and bank

density. The private saving rate is also affected by the public saving rate. Granger

causality tests point to a bi-directional causality between saving and growth. They also

carry out the Forecast Error Variance Decomposition (FEVD) analysis using the Vector

Auto Regression (VAR) framework. The FEVD results confirm the causality results

obtained using the Granger causality tests as well as the estimated saving functions.

Slami and Sheikhy (2008) examined the model that explains the behavior of household

saving in Algeria from 1970 to 2005. They tested several models using the Least Squares

method. The models are: the classical model, Keynesian model, relative income model, permanent income model, life cycle model, and the Taylor model. They found that a permanent income model is the best model to explain the saving behavior of the

individual in Algeria. According to this model, saving is determined by previous saving

and current available income.

Nwachukwu and Egwaikhide (2007) examined the determinants of private saving in

Nigeria. Their study re-estimated Nigeria’s saving function with the Error Correction

Model (ECM) using annual data from 1970 to 2005. It also compared the estimation

results of the ECM with those of three conventional models: Partial-Adjustment, Growth

Rate, and Static Models. The conclusion was that the ECM performs much better than the

other models. Its results reveal that the saving rate is correlated positively with the level

of disposable income but correlated negatively with the rate of growth of disposable

income. The real interest rate on bank deposits has a significant negative impact, while

255 public saving seems not to crowd out private saving. Thus, the Ricardian Equivalence

was found not to hold in Nigeria as opposed to what obtains in industrialized and semi-

industrialized economies. Furthermore, external terms of trade, the inflation rate, and the

external debt service ratio have a positive impact on private saving.

ElBassam (2005) estimated a model that analyzes household saving in Saudi Arabia from

1970 to 2002. After reviewing a number of saving theories and field studies on household

saving in Saudi Arabia and other developing and developed countries, a partial

adjustment model of the Koyck type was determined to analyze the behavior of

household saving in Saudi Arabia. This model employs several explanatory variables:

income, inflation, dollar interest rate, wealth, financial development, dummy variables,

and the dependent variable (household saving) lagged one year. The study tested the fit of

the model which indicated that the estimated model is statistically significant. The

estimated model of this study indicated that the income, wealth, and financial

development variables are statistically significant, while the other variables (inflation,

Dollar interest rate, and financial intermediation) are found statistically insignificant and

therefore were removed from the model. Regarding the stability of the estimated model, a

Chow test indicated that the model was unstable during the period of this study.

Moreover, the use of the dummy variable revealed that a shift in the intercept of the

model during the period 1973 to 1982 and the period that followed it. Finally, the

empirical results of this study proved that the Gulf crisis had an effect on the household

saving behavior in Saudi Arabia.

Taher (2001) studied the determinants of private saving in Saudi Arabia since it is one of

the main factors that lead to the reduction of the current account deficit. He examined

256 private and household savings from 1974 to 1994 using multiple regression. He found

that government spending on investment has a positive effect on private saving, while budget surplus, excess of consumer loans, and outgoing remittances have a negative

effect. Consequently, he recommended that the government should expand infrastructure

investment, limit consumption loans, and encourage non-Saudi nationals to invest their

savings domestically.

Burnside (2000) analyzed the behavior of private saving in Mexico from 1980 to 1995

using quarterly data. The correlation of the private savings with various macroeconomic

time series suggested that private saving is very much correlated negatively with the

fiscal balance of the government. In addition, private saving is more highly correlated

with the level of private disposable income, which has significant cyclical components,

than it is with GNP. This suggests that saving, rather than consumption, contains

transitory movements in income. The paper used vector auto-regressions in which tax

shocks are found to be the primary determinants of the ratio of private savings to GDP.

Other important determinants of saving in Mexico are the shocks to the world oil prices

and to the level of output in the U.S. and Mexican monetary policy shocks and the world

oil price.

ii. Literature on Saving in Egypt

Giavazzi et. al. (2010) studied the behavior of household saving using household-level

micro data from the Egyptian Income, Expenditure, and Consumption Surveys. As

opposed to the aggregate time series, the survey data clearly points to a reduction in

household saving, from 14 percent in 2004/05 to 10 percent in 2008/09. On investigation

of the decline in savings they found evidence that the decline in saving might have been

257 due to improvements in access to finance and credit. They found a negative correlation between saving and measures of local financial development. The study also suggested

that the pension reform considered in Egypt—in which contributions to pension funds are

compulsory—is less likely to result in the saving shortfall observed in countries where

individuals were left with the option of saving for retirement.

Hevia and Loayza (2012) illustrated the mechanisms linking national saving and

economic growth in order to understand the possibilities and limits of a saving-based

growth agenda in Egypt. The study used a simple theoretical model, calibrated to fit the

Egyptian economy, and simulated exploration of different potential scenarios. Their main

conclusion was that if the Egyptian economy does not experience progress in productivity, then a high rate of economic growth is not feasible at current rates of

national saving and would require a saving effort that is highly unrealistic. To obtain a

constant 4 percent growth rate of GDP per capita, with no Total Factor Productivity

(TFP) improvement, a national saving rate of around 50 percent in the first decade and 80 percent in 25 years would be required. However, if productivity starts to rise to moderate

levels at least, sustaining and improving high rates of economic growth become viable.

To achieve the goal of high economic growth, the national saving effort can only be

alleviated realistically by forceful and purposeful productivity improvements.

Love (2010) investigated corporate savings in Egypt which represent 50 percent of total

savings. Her study focused on two measures of corporate savings – financial savings and

258 28 physical savings P27F .P It employed two datasets—Investment Climate Assessment and data for publicly listed firms on the Egyptian Stock Market (EGX) to analyze the determinants of corporate savings behavior. The study found that larger firms invest more (physical saving), which is essential for maintaining competitiveness and growth. In addition, small firms invest less because of the lack of access to credit. Thus, improving access to credit for small and medium enterprises is likely to have a positive impact on investment. Moreover, despite the financial deepening, the use of credit products was declining during the last decade. The main policy recommendation from the study reaffirms the importance of improving access to financial services in Egypt. In addition, policies aimed at reducing macroeconomic volatility are likely to result in increased investment. Touny (2008) showed that Egypt’s domestic savings ratio has an average of only 14.6 percent of GDP through the period 1980–2005, which is poor in relation to other countries at a similar level of per capita income. Touny examined the long-run determinants of domestic saving rates in Egypt through the period 1975–2006. He used unit root and co-integration tests, which allow for heterogeneity in parameters and dynamics. The results of the study provided evidence that domestic savings in Egypt is determined by the following factors. First, the growth of per capita income has a positive influence on domestic savings in the short- and long-runs. Second, budget deficit ratio has a negative effect on domestic saving ratio. This means that higher government savings partially crowd out private savings, and thus do not support the existence of a full Ricardian Equivalence (See Ricardo 1951). Third, financial depth, as proxied by the increase in the ratio of broad money supply (M2) to GDP, shows a positive and significant effect on domestic savings in the long run. Fourth, the Real Interest

28 Physical savings represent addition to physical assets as investment in property, plant and equipment. Financial savings represent addition to financial assets, as increase in the firm‘s holdings of cash and marketable securities.

259 Rate (RR) has a positive effect in the short-run although the effect of RR in the long run

appears to be positive but statistically insignificant. Fifth, the inflation rate has a positive

and significant impact on the level of domestic savings in Egypt in both the short run and

the long run. This provides support of precautionary motives for saving in the face of

increased economic uncertainty in Egypt. Finally, the current account deficit recorded a

negative and statistically significant effect in both the short run and the long run, which

implies that external saving may tend to act as a substitute to domestic private saving.

AL-Mashat (2002) assessed the effects of financial sector reforms on non-government

savings in Egypt through the period 1970–1999. The quantitative outcomes of financial

sector reforms are evaluated by comparing relevant indicators before and after the

introduction of reform measures and by analyzing the link between those indicators and

the non-government savings rate. The measures of financial sector reforms include (1)

the cost of capital approximated by the RR, (2) the volume of intermediation measured by the ratio of M2 to GDP, and (3) the effectiveness of financial intermediation proxied by the ratio of reserve money to total deposits and reserve money to quasi-money with a

decrease in the ratios, indicating a growing efficiency of the banking system in

mobilizing deposits. The model is estimated for the whole period 1976–1999, the pre-

reform period 1976–1990 and the reform period 1991–1999. For the entire period, among

the financial intermediation variables, only the reserve money as a percentage of total

deposits is significant. This may reflect the dominance of “forced savings” and rationing,

as well as the importance of the public sector. For the pre-reform period, among the

financial intermediation variables, both the reserve money as a percentage of total

deposits and credit to the private sector are significant. During the reform period, all the

260 financial intermediation variables are significant (except M2/GDP). This suggests that

after reforms under the Structural Adjustment Program the savings ratio became more

responsive to changes in the effectiveness of the financial intermediation. Touny

concluded that as market-based financial systems are established, higher real returns on

savings, greater efficiency of the financial system, and a larger share of resources,

intermediated through financial institutions, all contributed to higher saving.

Hussein (2002) tested the impact of financial liberalization proxied by the expected real

interest rate on financial development proxied by financial savings in Egypt over the time period 1967–1996. The study used Auto-regressive Distributed Lag (ARDL) procedure

developed by Pesaran, Shin, and Smith (1995, 1999) and Pesaran and Shin (1999).

Hussein found that interest rates have a positive impact on financial savings. However,

the magnitude of the interest elasticity of financial savings in Egypt is too small in cases

where an increase of 1 percent in the interest rate causes an increase in financial savings by 0.08 percent and 0.04 percent in the long- and short-terms, respectively. He also found

that real per capita income and the expected inflation rate have a positive effect on

financial savings in the long-run. He concluded that an increase in the RR is not the

essential route to develop and enhance the financial sector in Egypt.

Elsayed (1993) studied the determinants of savings in Egypt and the factors that

contributed to raising saving rates during the period 1969–1990. Her study focused on the

determinants of domestic savings (gross and private) using stepwise regression. It

employed the following determinants: real per capita income, the inflation rate, per capita

cash balances (money, quasi money, and saving deposits), nominal and real interest rates

on one-year deposits, the unemployment rate, and the international trade rate. Elsayed

261 found that savings are determined by income and that it is correlated negatively with the

unemployment rate and interest rate. On the other hand, the effect of inflation is

undetermined. She concluded that implementing government economic reform policies that

have liberalized the interest rate since the 1980s, in addition to the economic regression

Egypt is currently witnessing will lead to a further decline in the saving rates.

iii. Potential Areas for Improving Empirical Investigation of Savings Behavior in Egypt

• Ensure higher quality of data: This research employs the quarterly data series

covering the period 1990–2010. The gross private savings series that was

introduced wasn’t available to much of the earlier research on savings in Egypt.

Most of the earlier studies on savings in Egypt resorted to studying aggregate

29 savings, P28F P while others attempted to use households and private sector time

deposits as proxies for nongovernmental savings. This, in turn, questions the

reliability of the analysis of earlier research. This is further detailed in the

dedicated section on variables selection.

• Adopting more robust modeling strategy: The majority of earlier studies on

30 savings behavior in Egypt adopted a single equation modeling approach. P29F P This, in

turn, triggers non-parsimonious models and flawed inference in serial correlation

and endogeneity. This research will adopt a modeling strategy that will seek more

robust avenues for modeling and analysis deploying VAR and ECMs that tackle

multiple problems of inefficiency and biased inference of earlier research, and

29 Appendix (I) provides a comprehensive literature review on earlier empirical work determinants of savings in Egypt from a macroeconomic perspective, this includes El Sayed (1993), Hussein (1999), Al- Mashat (2006), Touny (2008) 30 With the exception of Al Mashat (2006) that employed a VAR methodology, the other 5 studies adopted a single equation modeling approach.

262 uses the rich information embedded in the series with reference to the system as a

whole.

3. Theoretical Motivation

The Classical thought defined savings as the portion of income that isn’t spent on

consumption. It is an increasing function in the interest rate.

The Keynesian school considered savings as the hoarded portion of the income; hence it is a function of current income. On the other hand, current consumption is also a function of current income with a marginal propensity to consume less than unity. This school of thought assumes that the marginal propensity to consume and save is equal to unity.

Keynes argues that savings are not automatically invested, since those who save are

different from those who invest. He defined savings as a linear function income.

Accordingly, savings is defined in terms of absolute income level. An increase in total

savings leads to decrease in total consumption, resulting in a decrease in the effective

demand at the macro level (Keynes 1937). This will result in a fall in private investment,

and the economy may go into a recession. Keynes also points out that investment leads to

an increase in the national income, resulting in saving. Therefore, there is a univariate

direction of causality from investment to saving, not the opposite. The macro economy is

in equilibrium when saving and investment are equal.

The neoclassical theory holds that saving is a function of interest rate. All saving is

assumed to be invested. Therefore, economic growth depends on saving. The neoclassical

theory agrees with Keynes that the equality of saving and investment is the

263 macroeconomic equilibrium condition. However, the Solow-Swan model of growth

weakens the conventional view by showing that the saving decisions of the community

are irrelevant for the determination of the rate of growth.

The Permanent Income Hypothesis (PIH) of Friedman was another development in the

savings literature. It identified two key components of income. The first component is the

permanent income that is defined as the expected income over a planning period with a

steady rate of consumption maintained over lifetime that is subject to the present level of

wealth. The second component is the transitory component of income. This is the

difference between actual and permanent income. The main argument of this hypothesis

is that individuals are assumed not to consume out of the transitory component of

income. Accordingly, the marginal propensity to save transitory income will be unity.

Empirical tests of the PIH are mainly concerned with the effect of initial wealth on

savings as well as the marginal propensities to save out of permanent and transitory

components of income. However, the results of empirical studies are mixed for both

developing and industrial countries.

This was followed by Life Cycle Hypothesis (LCH) that enriched the theoretical literature on savings. The key assumption of this hypothesis is that individuals spread their lifetime consumption evenly throughout their lives by accumulating savings during earning years and maintaining consumption levels during retirement. Tests of the life cycle hypothesis are therefore mainly concerned with the effect of demographic

variables such as age groups, birth rates, and dependency ratios on savings behavior,

mainly in the long term . The second group of variables used to describe savings during

working life and dissavings during retirement are financial variables such as interest

264 rates, inflation rates, available financial instruments, and initial wealth levels which

affect the intertemporal consumption decisions of households.

The life-cycle model of household savings behavior, grounded in expected utility theory,

is generally associated with Modigliani (Modigliani and Brumbreg, 1954; Modigliani and

Ando, 1957) and Friedman (1957). Modigliani and Friedman made their contributions by

translating the abstract notion of an optimal consumption profile into a model that could be estimated econometrically (King, 1985). This demonstrated the empirical relevance of the separation of the consumption and income profiles, which is the principal implication of the life-cycle model. Modigliani’s emphasis on the role of wealth in the consumption function and Friedman’s concept of “permanent income” are based on the idea that current consumption is not determined primarily by current income.

Nevertheless, empirical tests of these two major hypotheses focused on cross country studies that had many drawbacks (Muradoglu and Taskin 1996). A key challenge is regarding comparability of samples that include countries at different levels of development. This questions the reliability of these studies to interpret differences across country groups (see Ortmeyer 1985; Koskela and Viren 1982, Schmidt-Hebbel and

Servén 2000, and Beneston and Kauffman 1990). Moreover, (Schmidt- Hebbel, Webb, and Corsetti 1992) emphasized another challenge that arises when using national aggregate savings data that assumes that private savings account for a predominant part of total savings. They emphasized that an aggregation of savings is inconsistent across countries, which gives rise to cross-country inconsistencies due to the difference in the methodology employed in their derivation.

265 4. Hypothesis to be Tested, Variables Selection, and Data Issues

This chapter follows the Modigliani and Brumbreg (1954) LCH motivation as adopted by

the Loayza, Schmidt-Hebbel, and Servén (2000) specification for empirically testing for

determinants of savings. I test the null hypothesis ( ) that aggregate private savings in H Egypt vary positively with real per capita income and the interest rate, yet it varies negatively with inflation and the level of financial development (M2 to GDP).

The following equation represents my regression equation with expected signs shown between brackets based on theory:

( ) ℎ (+/-) , 2 (-) , , (-) , (+)

(1)

The quantitative modeling methodology for this chapter will be very similar to the proposition in the first essay. This chapter will investigate short- and long-run

relationships between variables of interest by following the same steps proposed earlier

when estimating the money demand equation.

This includes the attempt to investigate the relationship between variables of interest

through adopting a VAR modeling procedure in addition to the adoption of the (Johansen

1988) test for co-integration and employing a vVECM as proposed by (Johansen 1988)

and (Johansen and Juselius 1990).

266 Real Gross Private Savings Per Capita

Gross private saving is generally computed as the difference between gross national

saving and the relevant definition of gross public saving (Loayza, Schmidt-Hebbel and

Servén 1999). The applicability of this definition is challenged in developing economies

that suffer from a weak national accounting framework. Accordingly, saving behavior in

developing countries has been examined predominantly by using multi-country cross-

sectional data which produced dubious results, given vast differences among countries

with respect to the nature and quality of data. This makes cross-country comparison and policy recommendations fraught with danger (Athukorala and Sen 2001). This was the

main limitation for studying private savings behavior in Egypt. As discussed in the

literature review section, Al-Mashat (2006) used non-government time deposits to GDP

as a proxy for private savings to assess the relationship between financial sector

development and growth. Hussein (2002) used savings/GDP as a proxy for financial

development, where financial savings are the sum of demand, time, and savings deposits

held at deposit money banks to assess the response of savings rate to the liberalization of

interest rates in Egypt in early 1991. The other three studies on savings in Egypt focused

31 on gross domestic savings that included both public and private savings P30F .P

Arrieta (1988) emphasized the need to use accurate savings data in modeling private

savings behavior in developing economies as a pre-requisite for conclusive findings in

this field for a larger number of LDCs. This seems more important particularly with

respect to data on domestic private savings which, as has been noted, constitute more

appropriate dependent variables in order to test the theoretical models. Moreover, it will

31 See ElSayed (1993), Touny (2008) and the literature review section.

267 be necessary to overcome data limitations which prevent specifying and testing the

linkages between rates of interest, and saving in individual country studies to contribute

to better understanding of the dynamics of this relationship.

This research improves on earlier studies and fills a significant gap on empirical

modeling of private savings in Egypt. It employs a new time series on private savings in

Egypt that was compiled by the CBE and the Central Authority for Public Mobilization

and Statistics (CAPMAS) in Egypt. This series includes savings in deposits money banking, post offices, specialized banks, investment and savings certificates issued by banks, in addition to institutional private savings in pension funds and insurance companies. These were significant components that accounted for almost 15 percent of aggregate private savings during the period 1990–2010.

While the new series is available in annual frequency, the Chow Lin’s Temporal

Disaggregation Methodology (Chow and Lin 1971) is employed to disaggregate the annual data into higher frequency quarterly series. This methodology generates the best linear unbiased interpolation, distribution, and extrapolation of the time series by using a related series called the indicator variable. The Chow Lin method uses quarterly indicators to generate the quarterly volatility report and transform annual frequency data to quarterly data. To generate a quarterly series of aggregate private savings; the quarterly series of non-government demand and time deposits, obtained from the IMF – IFS 2015,

32 was used as an indicator variable. P31F

32 Details of the matlab code and output for the disaggregation process are available upon request. Matlab 7.10.0.499 was used for disaggregation.

268 The generated quarterly series was deflated using the Consumer Price Index (CPI) and

divided by the population count in order to obtain the real gross private saving per capita

in logarithm.

Motivated by the Life Cycle Model; what follows is a brief account of relevant variable

selection and hypothesis to be tested with respect to the saving behavior in Egypt.

Real Income Growth Per Capita

Economic Theory provided various interpretations to the nature of the relation between private savings and income growth. The life-cycle theory of saving and consumption predicts that changes in an economy’s rate of economic growth will affect its aggregate

saving rate. In the simplest version of the model, in which young people save for

retirement and old people consume their previously-accumulated assets, an increase in

the rate of economic growth will unambiguously increase the aggregate saving rate, because it increases the lifetime resources (and saving) of younger age groups relative to

older age groups. This was supported by Chow and Lin 1971, among others.

However, more complicated and realistic versions of the life-cycle model yield

ambiguous conclusions about the relationship between saving and growth. For example,

young people may have low current income but high life-time wealth, and may therefore borrow to finance current consumption.

Accordingly, whether higher growth increases or decreases the aggregate saving rate

depends on whether the age-profile of saving is correlated negatively with age, which is

269 an empirical matter (see Poterba (1994), Paxson (1996), Deaton and Paxson (1997), and

Nwachukwu and Egwaikhide (2007)).

Following Modigliani and Brumberg (1954) and Modigliani (1998) the increase in the

aggregate growth of real per capita income—not the level—is considered in the proposed

setup as a determinant to private savings. The difference log of real per capita income is

included in the model.

Both Real Gross Private Savings and Real Income are controlled for population growth by considering both variables in Per Capita terms. Due to the unavailability of high

frequency data on population, the annual population count is used to index both quarterly

series.

Accordingly; the nature and magnitude of the relation between growth and savings will be tested using Egypt’s data and will add an explanation to the ongoing debate.

Financial Development

The earlier work of McKinnon (1973) and Shaw (1973) and the large body of

33 contribution that followed, assumed financial development to enhance the saving rate. P32F P

Financial development policies call for the elimination of credit ceilings, interest rate

liberalization, ease of capital flows, enhanced prudential guidelines and supervision, and

the development of capital markets.

33 See Rostom (2008) for a comprehensive literature review on financial development and growth.

270 Loayza and Shankar (2000) empirically showed that, from a portfolio composition perspective, financial development allowed the private sector to increase the long term assets and durable goods within their portfolios of financial assets.

Loayza et al. (2000) proposed separation of the effect of financial development on saving rates into a direct short-run impact, which is usually negative, and an indirect long-run impact, which is generally positive. The direct effect consists of price and quantity channels. The price channel works through higher interest rates and, although popularly advocated in operational documents and the financial press, is seldom effective empirically in raising private saving, suggesting that the negative income effect of higher interest rates tends to neutralize the positive inter-temporal substitution effect. Expanding access to credit to previously credit-constrained private agents, allows households and small firms to use collateral more widely and reduce down payments on loans for housing and consumer durables. Theory predicts that this move should reduce private saving because individuals are able to finance higher consumption at a given current income level.

However, whether increased financial development itself significantly increases an overall propensity to save depends on the extent of substitution between financial saving and other items in the financial assets portfolio. Consequently, the expected signs of this relationship in the private saving function are ambiguous (Athukorala and Sen, 2004).

Real Interest Rate

The net effect of the interest rate on saving/consumption is unclear in the Life Cycle

Model. A higher interest rate increases the present price of consumption relative to the

future price (the substitution effect), and thus provides an incentive to increase saving

271 (Athukorala and Sen, 2004). If the household is a net lender, the interest rate rise also

raises lifetime income, and thus tends to increase consumption and decrease saving (the

income effect). This saving responds positively to rises in the interest rate only if the

substitution effect is stronger than the income effect. It could be argued that, for the

typical developing economy, the net impact of a change in the real interest rate on saving

is likely to be positive.

McKinnon (1973) and Shaw (1973) also argued that the relationship between RR and

saving is positive for a developing economy. The argument hinges on the state of

underdevelopment of financial markets in these countries. Financial intermediation in

these economies is dominated by self-financing and bank loans. This influences decisions

on financial saving that are determined more in this case by the desire to invest rather

than to live on interest income. As a result, the greater part of household saving will be in

the form of cash and near-money assets, and the substitution effect will usually be much

greater than the income effect of an interest rate change.

In January 1991, banks’ lending and deposit rates were liberalized in Egypt. The ceiling on bank lending to the private sector and bank-specific ceilings on lending to the public sector companies were abolished in October 1992 and July 1993, respectively. This gave a boost to the public’s confidence in the government’s reform program. Lifting restrictions on market entry and increased competition have stimulated the efficiency of bank intermediation. Moreover, public sector companies were allowed to deal with all banks without prior permission from a public sector bank.

272 In the context of a developing economy, the hypothesis of a positive relationship between

real interest and real private savings in Egypt will be tested in the following section.

Inflation and Macroeconomic Uncertainty

The impact of inflation on saving—within the setup of the Life Cycle Model—prevails

upon determining real returns to saving (the RR). This proposition is based implicitly on

the assumptions of inflation neutrality and absence of money illusion in saving behavior

and the absence of the real balance effect of inflation. However, the validity of this

assumption is questioned in countries with underdeveloped financial markets for two

main reasons.

The first is that inflation brings about uncertainty in future income streams and can lead

to higher saving on precautionary grounds. This may be particularly true for households

in developing countries whose income prospects are much more uncertain than their

counterparts in developed countries. Second, inflation could influence saving through its

impact on real wealth.

If consumers attempt to maintain a target level of wealth or liquid assets relative to

income, saving will rise with inflation. For these considerations, we include the inflation

rate as an additional explanatory variable (see Deaton (1989)). It is calculated as the

quarter over quarter percentage change in CPI.

The second is motivated by the fact that an individual's level of precautionary saving is

modeled as being determined by the utility maximization problem. This was realized by

Friedman (1957), and later by Bewley (1977) in their seminal work on the PIH. Ando and

273 Modigliani (1963) emphasized the relevance of the life-cycle framework, which therefore, builds on an inter-temporal allocation of resources between the present and an uncertain

future with the goal of maximizing utility. Rational individuals take sequential decisions to

achieve a coherent and ‘stable’ future goal using currently available information. Skinner

(1988) and Zeldes (1989) observe that an increase in uncertainty should raise saving since

risk-averse consumers set resources aside as a precaution against possible adverse changes

in income. Carroll (1991) shows that uncertainty helps to explain why consumption is

highly correlated with income in the case of young consumers who expect their incomes to

increase in the future but do not know by how much.

Uncertainty also explains why the older population saves a positive amount as they face a

lot of uncertainty regarding the length of their life and health costs. Carroll and Samwick

(1995) obtained results which suggest that precautionary saving may account for a large

chunk of household wealth. Loayza, Schmidt-Hebbel, and Serven (2000) find a positive

and significant relationship between inflation and the private saving rate.

The hypothesis of a negative relationship between real private savings and inflation will be tested to assess the aversion of the private savers to future uncertainty.

5. Economic Methodology and Modeling Strategy

Inspecting the stochastic properties of the variables is the first step in analyzing the

relationship between the variables. This procedure involves visual inspection of variables

to check if the series are trending smoothly. The smooth trending pattern makes it

feasible to quantitatively test for the presence of unit roots following the Augmented

Dickey Fuller (ADF) procedures (see Dickey and Fuller 1979, Campbell and Perron

274 1991, and Enders 1995). This step is vital as it determines the strategy and methodology for modeling. Using variables with unit roots in linear regression violates the basic assumptions needed for asymptotic analysis and consistency of estimation as it leads to spurious results (Davidson and Mackinnon, 1999).

The unrestricted (or general) form of the ADF (1981) unit root test equation is:

k 3 D = + + a D + g +y +x yt b0 1yb t- 1 ∑ i y - it ∑ iCSit trend t i=1 i=1

(2) x trend is time trend, is a white noise error

The ADF test maintains the null hypothesis of non-stationarity of the given time series. If test results provide evidence on the presence of unit roots in levels of the series, the test is repeated for differenced series to identify the order at which the series is stationary. This is defined as “order of integration of the series”. Variables of interest interact to enter into equilibrium in the long run if they are integrated of the same order. This is the key objective of this dissertation that attempts to test the nature of the equilibrium relationship between the real monetary aggregates, the real sector, and the external sector.

Accordingly, a system of equations will be modeled to investigate the existence of a stable relationship between the variables of interest employing a VAR procedure and

34 testing for optimal lag length using the general to specific criteria P33F P (see Enders 1995).

This is complemented by inspecting the graph of residuals and distribution visually, as well as residual diagnostic tests to ensure that residuals are white type and do not

34 The optimal lag length is defined as lags are reduced without losing information.

275 influence the parsimony of the model. The Chow test and break point test for model

stability are also implemented in order to ensure consistency of estimation and inference.

The Johansen (1988) and Enders (1995) test for co-integration is employed when

ensuring that the series are integrated of the same order. Tests of weak exogeneity will

also provide evidence on the nature of the long run equilibrium relationship between

variables.

The Full Information Maximum Likelihood (FIML) of a VECM is as follows:

k- 1 D = P + G D + m + e yt yt- 1 ∑ i y - it t i=1 (3)

y m Where, t is a (n x 1) vector of the n variables in interest, is a (n x 1) vector of

e constants, G represents a (n x (k-1)) matrix of short-run coefficients, t denotes a (n x 1)

vector of white noise residuals, and P is a (n x n) coefficient matrix. If the matrix P has

reduced rank (0 < r < n), it can be split into a (n x r) matrix of adjustment coefficients a , b and a (n x r) matrix of co-integrating vectors . The former indicates the importance of

the co-integration relationships in the individual equations of the system and of the speed

of adjustment to disequilibrium, while the latter represents the long-term equilibrium P =a b ¢ relationship, so that where k is a maximum number of lags in VAR; t denotes time and D is a difference operator.

Testing for co-integration, using the Johansen’s reduced rank regression approach,

centers on estimating the matrix P in an unrestricted form, and then testing whether the restriction implied by the reduced rank of P can be rejected. In particular, the number of

276 the independent co-integrating vectors depends on the rank of P which, in turn, is determined by the number of its characteristic roots that differ from zero. The test for nonzero characteristic roots is conducted using Max and Trace tests statistics. It is important to note that as Johansen (2002) shows, since these tests tend to reject the null hypothesis of no co-integration in small samples, it is useful also use the degree of freedom adjusted test statistics in the co-integration analysis.

A VECM is then determined following Johansen (1988) and Johansen and Juselius

(1990). The last step would be identifying a conditional vVECM, including endogenous variables that rejected the null hypothesis of being excluded from the co-integrating relation.

This model will be tested for stability and residuals should be ensured as non-serially correlated. The analysis will move on to determining the short-run relation through running the VECM. The General to Specific Modeling Approach is applied. Note that general idea behind this approach is: (1) to estimate the general/unrestricted VECM with the maximum lags of the right hand side variables:

k- 1 k- 1 D =a +a + a D + a D +n xt 0 y ECMt- 1 ∑ 1i x - it ∑ 2i z - it yt i=1 i=0

(4)

x z Where, t is a dependent variable, t stands for a vector of explanatory variables,

ECM t- 1 is one lagged the residuals from the long-run model, vRyt R– is the residuals which

are expected to be white noise. If α R yR falls in the interval of (-1 0) and is statistically

277 significant, then it can be concluded that the variables exhibit a stable co-integrating relationship and short-run fluctuations are corrected to the long-run equilibrium.

The coefficient of the lagged error correction term (α) is expected to be negative and statistically significant for the model to work and converge to the equilibrium path in the long run. This model should be tested also for stability to ensure the parsimony of the outcomes. (b) The appropriate lag length will be determined based on the general to specific reduction methodology, and the model will be tested also for stability and well define the nature of the relation between the four variables in the short and long runs. The exclusion of statistically insignificant variables, and performing the comprehensive set of autocorrelation, serial correlation, normality, heteroscedasticity, and misspecification tests are key to reaching a more parsimonious specification for the model. For this deliberation, Oxmetrics 6.1 will be used as the key econometric package for related quantitative modeling.

6. Empirical Results

The unit root tests lay the foundation for the VECM framework outlined above. As indicated above, this dissertation will adopt an ADF procedure to test for unit roots. The test will incorporate both constant and trend to account for trend stationarity. Since the

278 data is in quarter-annual frequency, seasonal adjustment is required. The lag length for

ADF test is determined based on the Schwert (1989) formula for annual data:

= 6 100 (5)

where T is the number of observations and INT stands for integer value. Since T = 84, the

lag length suggested by this criteria is 6 lags.

All tests are conducted by Oxmetrics 6.1. Tables (31), (32) and (33) provide summary

statistics of the ADF test. Following the Dickey and Pantula (1987) sequential approach

to confirm the order of integration for each variable, unit roots are tested under the

assumption that the order of integration is at most two. The second difference for the data

is employed for this purpose and as shown in table (31), data provide strong evidence to

reject the null hypothesis of non-stationarity at a 1 percent level for all variables.

This is replicated for the first difference of the series. The null hypothesis of existence of

a unit root is rejected for all variables at a 1 percent significance level. This indicates that

the time series are stationary in their first and second differences.

Moreover, upon replication of this procedure with levels of data, the null hypothesis can’t be rejected for all variables, with the exception of the change in the nominal exchange

rate. This step concludes the existence of strong evidence that all the data series robustly

follow an I(1) process in levels with the exception of the change in the exchange rate that

follows an I(0). The quantitative exercise is complemented by a visual inspection to

double check this conclusion. As seen in graphs (32, 33a and 33b) for the closed

279 economy specification and (12) for the open economy specification; there are no

noticeable structural breaks in the series as they are smoothly trending upward and

downwards. This confirms the conclusions from the unit root tests.

ii. Cointegration Analysis

At this stage the set of relevant variables, posited by the LCH for savings behavior proposed by Modigliani & Brumbreg, (1954), is considered. The standard econometric

theory concludes that regressions for non stationary variables give spurious results. The

residuals of these models will not be stationary and accordingly the standard Ordinary

Least Squares (OLS) assumptions will not hold. This applies to the Egyptian data that

was tested based on the ADF procedure for integration in the previous section and found

to possess unit roots and exhibit integration of first order [I(1)]. However, some linear

combination of these series has a lower order of integration. There will also be some (co-

integrating) vector of coefficients that forms a stationary linear combination of these

variables.

The aforementioned analysis confirmed the fact that levels of real gross private savings per capita, RR, and m2toGDP variables are integrated of the same order (I(1)). This is

considered the first necessary condition needed in order to investigate the existence of a

long run relationship between these three variables. The existence of an equilibrium long

run relationship between these three variables would mean that the variables are co-

integrated. The definition of the co-integration relationship can be explained by the fact

that it is the set of linear restriction—in other word the linear relation—by which a

number of non stationary—integrated—series are combined to produce a stationary

280 series. This stationary series is then analyzed to show how variables move and adjust in

the long run equilibrium.

It will be plausible then to investigate the presence of a long run equilibrium relationship between real gross private savings per capita, RR, and m2toGDP. A VAR model in levels

is considered between these three series that were proven to be integrated of order one

(I(1)). One advantage of level VAR models over alternative models is that the former are

robust to the number of unit roots in the system. This robustness is one of the reasons

why level VAR models are used extensively in applied macroeconomic research,

including our case (Qureshi, 2008).

After the identification of the nature of the long run relationship and specifying the error

correction term, the next step is to identify the short term dynamics between the first

differences—stationary—variable of interest (real gross private savings per capita is

considered for this chapter) and the first difference of other determinants, including those

that were I(0) at levels and excluded from long run modeling, including growth in real per capital income and inflation.

The linear representation of the variables in the long run is often called the Granger

theorem (Granger, 1983). Moreover, (Engle and Granger, 1987) proposed a methodology

to map a one to one relationship between co-integration and a VECM. The VECM

defines the short run and the long run relationships between variables. In other words, it

estimates how changes in levels of variables are matched by a change in the level of the

variable of concern. These estimations are considered consistent and highly efficient.

281 iii. Selecting the Lag Length

The reliability of the findings of the VAR system of equations depends on the lag length

considered in running the system. This research adopts a robust procedure to test the

optimal number of lags that maximize the efficiency of the model and ensure that the

feedback information is adequately and efficiently maintained within the model. The

optimal lag length is tested using several criteria including Akaike's (1973) Information

Criterion (AIC), Shwartz Criteria (SC), and the Log Likelihood test. Business Cycles

Theory proposes that according to the quarterly data the propagation of the business cycle

is considered within a six-year period (Cotis and Coppe, 2005).

Analysis starts with a VAR system using six lags and then a sequential reduction process

is implemented to move from a general to a specific model to avoid over- parameterization and ensure parsimony. The results of the F-test statistics shown in (table

34) confirm that three lags reject the null of loss of information upon sequential reduction

of the rank of the VAR from six lags to three lags. The SC, Hannan-Quinn (HQ) and AIC

are providing mixed results. AIC individual F tests for specific lags provide evidence in

favor of a four lags system. However, SC and HQ joint significance provides evidence

against adopting this lag length, calling for one and two lags consecutively. The relevant

systems of rank 4 either suffered from problems of serial correlation or

heteroscedasticity. On another front, the set of F tests of joint significance calls for

adopting three lags as optimal lag length that avoids loss of information. Also, reduced

models with two lags and a single lag perform worse than the three lags models in terms

of residuals diagnostics. Residuals consistently exhibited serial correlation that

282 invalidated results of relevant models. Accordingly, the reduced three lags rank model is further vetted below to validate the reason for choosing this rank.

The residuals of the reduced rank—three lags—VAR is provided in table 36. The residuals’ misspecification test provides evidence that the multivariate Portmanteau test’s null hypothesis up to lag 3 fails to reject the null—of iid—at 10 percent. This is the best result compared to the two lags and four lags models. This test inspects whether residuals exhibit white noise independently and identically distributed (iid) white noise characteristics. However, it fails to reject the null for individual equations within the system. The null hypothesis of the multivariate Portmanteau test states that the autocorrelation functions of all series have no significant elements for lags 1 through 3, yet the main weakness of the test is that the alternative hypothesis isn’t well identified.

Accordingly, the test can identify the problem of lacking (iid) but it doesn’t provide further information about the nature of the problem. Moreover, the null hypothesis of

(iid) failed to be rejected for all individual equations. These mixed results require a look at alternative tests to ensure that the residuals are free from serial correlation that invalidates the results of the model.

Results of the restricted system as well as individual equations tests for AR 1-2 test’s and

ARCH 1-1 for the can not reject the null hypothesis of the presence of serial autocorrelation. Accordingly, the reduced VAR doesn’t suffer from serial auto correlation. Moreover the vector Hetero test fails to reject the null of homoskedasticity of the system’s residuals. This is the case also for a Hetero text of individual equations. The system normality test’s null hypothesis is rejected for the system at 5 percent. The null can’t be rejected for individual equations except for m2toGDP ratio as it rejects the null

283 at 1 percent. This is attributed mainly to a number of outliers. Upon inspection of graph

33b, it can be seen that the density of the residuals is close to a robust bell shape.

The restricted VAR stability and parameter constancy can be concluded confidently upon inspection of graphs 34a and 34b. Model constancy is tested by the Chow one step test and the break point test indicated in figure 23, in addition to the plot of plot of the companion matrix of the reduced rank model. The graph of the one step residuals also confirms these findings.

In conclusion, individual equation and vector misspecification tests for the reduced rank three lags model residuals and model stability robustly support our proposition to proceed with modeling long run and short run dynamics adopting three lags and conditioning the analysis on white type noise residuals. iv. Co-integration Analysis

The co-integration analysis is employed to the three lags system. Johansen’s co- integration approach is adopted. The constant enters the model as unrestricted and the nine dummies elaborated earlier are also unrestricted. Therefore, they don’t lie in the co- integrating space.

The rank of this matrix is then identified by the number of linearly independent rows or columns that define this relationship. For the purpose of our analysis, the full rank of this

284 matrix is 3, and the matrix is 3x3, since we have a five equation system and we are testing for the rank(r) of the matrix that identifies the number of co-integrating relationships through computing the likelihood for various eigenvalues of the matrix. The first block of table (37a) shows the results of the test, as the null that rank (r) equals zero rejects the null of a no co-integrating relationship, yet the null that (r) 1 fails to reject it. This is indicated by the p-values of the trace and maximal eigenvalue tests for both cases the asymptotic case and the finite sample case.

Results of the maximal eigenvalue ( ) provide strong evidence of a single λ cointegrating relation. The null hypothesis is rejected of no co-integration at a 1 percent significance level and trace eigenvalue ( )—adjusted for the degree of freedom— λ rejects the same null at a 5 percent significance level. Table (37a) also reports that a maximal eigenvalue test rejects the null at 5 percent for a second co-integrating relationship. The test statistic of the trace test and the adjusted tests indicate that the null hypothesis wasn’t rejected at all significance levels. This is complemented by the fact that the eigenvalue associated with the first co-integrating vector is certainly dominant over those corresponding to other vectors, thereby confirming that there exists a unique co-integrating vector in the model.

These results support a research decision to proceed confidently with the analysis given the existence of a single co-integrating relationship for the closed economy formulation. v. Long-run Modeling

We run a co-integrated VAR, then analyze the standardized eigenvectors. These are defined as the matrix containing the parameters of the co-integrating vectors. The rows of

285 the β matrix—as defined in the earlier methodology section—represent the standardized coefficients of the variables entering into the respective co-integrating vector. These coefficients are then normalized to unity along the principal diagonal of this matrix.

The estimation process is then followed by a set of tests for weak exogeneity and stationarity. Table (37d & e) shows the results of various hypothesis tests for statistical significance of beta coefficients (elasticities) in addition to the weak exogeneity tests of alphas.

The test statistics show that all betas—individually and jointly—aren’t statistically different from zero at 5 percent significant levels. This indicates that the long run equation for real gross savings per capita includes the RR and the financial development indicator in addition to a constant.

Moreover, when the joint statistical significance for betas is tested together with the alpha coefficient for gross savings per capita; the test fails to reject that these three coefficients are jointly different from zero at 5 percent. This ensures that the model converges towards equilibrium.

Further tests of statistical significance of alphas were conducted to determine the feedback effects of the co-integrating relationship. The real gross savings per capita own alpha was negative and statistically significant at a 5 percent level giving χ2 (1) =6.4004, hence a long run relationship can be identified.

286 The test statistics show strong evidence to reject the null that (financial development proxied by m2toGDP) is weakly exogenous and consequently its respective alphas re significantly different from zero with a 5 percent significance level.

The joint test for statistical significance of all alphas strongly rejects the null hypothesis at a 5 percent significance level giving χ2 (2) = 6.4985. This in turn confirms the fact that we can’t work with a single equation framework.

Further individual and joint significance tests were conducted and summarized in table

(37e) and indicate that the alpha of RR is statistically not different from zero at all significance levels. When tested jointly with all other betas and alphas, it can be concluded that the overall joint significance of all coefficients are different from zero at a

1percent significance level. Accordingly, real interest rate isn’t weakly exogenous.

It can be concluded that the feedback between the real gross savings per capita and the state of financial development in the economy provide the significant information needed to determine the long run relationship between these variables.

The long run equilibrium relationship for real gross savings per capita in Egypt is provided in equation 10 below.

The implied long run equilibrium relationship for real gross savings per capita (rgpspc) can be outlined (see table 37b):

rgpspc = 1.1317 - 0.045087 * RealInterestqoq + 0.40821* m2gdpratio

(6)

287 vi. Real Interest Rate

The RR negatively influences private savings in the long run in the case of Egypt. This interpretation should be considered with caution. As elaborated earlier, the empirical evidence on interest-rate elasticities of saving reflects the theoretical ambiguity, that empirical estimates typically are small and not significantly different from zero.

An interpretation to the overall negative relationship between real private savings per capita and RR can be attributed to the fact that income effect outweighs the sum of its substitution and human wealth effects.

The income effect is further interpreted by the fact that higher interest rates imply that fewer current dollars are needed to fund a given amount of future consumption. Planned future consumption is therefore less expensive, making people better off in a lifetime sense, and leading them to consume more today and save less. This effect dominates the substitution effect, which calls for increasing the amount of future consumption that is gained by foregoing a dollar of consumption in the current period. The substitution effect makes today's consumption more costly relative to future consumption. Based on the interpretation of this effect, the interest rate increase encourages people to consume less today and save more. The statistical results shown in the long run equation provide evidence that income effect dominates the substitution effect in the case of Egypt. This result is consistent with relevant less developed countries results by Loayza, Schmidt-

Hebbel, and Servén (2000).

288 The coefficient represents the semi-elasticity of real balances with respect to interest rates, and its value is - 0.045 if using percentages. At a 5 percent interest rate, the elasticity of money balances with respect to interest rates is -0.22. vii. Financial Development

Financial development positively influences private savings in the long term in Egypt.

The data provide strong evidence that the indirect positive effect of financial liberalization on private savings in Egypt should not be underemphasized. The positive implication of liberalizing domestic financial markets in Egypt—through recapitalizing domestic banks—fostering the efficiency of financial intermediation, in addition to liberalizing interest rates, has contributed to higher growth. Thus, it is through faster income growth, attaining higher levels of financial development, depicted by a 1 percent increase in m2toGDP increases private saving rates by 0.4 percent in the long run in

Egypt, if all other variables remain unchanged.

An alternative test for stationarity of real gross private savings per capita was conducted through testing if beta coefficients of (real interest rate and the financial development indicator -m2toGDP) are jointly different from zero. The null hypothesis was strongly rejected at a 5 percent significance level with χ2 (2) = 6.1963, thereby providing strong evidence that it is non-stationary.

The diagnostic statistics for the VECM’s system of equations indicate that the system is free from serial correlation since the Vector (SEM-AR 1-2) test statistic F(20,92) = 1.2829

[0.2108]. However, residuals suffer normality and heteroskedasticity which requires further analysis.

289 However, residuals for real gross saving equation are free from serial correlation,

heteroskedasticity, and normality issues. The financial development equation suffers non-

normality and heteroskedasticity due to a couple of outliers that can be visually inspected

in graph (37). The visual inspection of this graph also confirms the residuals are seen to be fairly normally distributed.

A research decision is made to proceed with the analysis since individual equations are

all free from serial correlation except for the difference equation for the interest rate

where the AR 1-2 F-test indicate the presence of serial correlation at a 5 percent

significance level, yet the ARCH1-1 test indicates the opposite.

The model was also tested for overall stability. It can be visually inspected in graph 35 and

36 that the overall model is stable and fails to reject the one step Chow test (1up) and the breakpoint test (Ndn) of stability at a 1 percent significance level. Moreover, the individual

coefficients were tested for stability, and it can be confirmed from graph 35 that the

coefficients are fairly stable.

Upon graphing the roots of the companion matrix they all lay within the unity circle and

fairly near the middle. It can be concluded confidently from the aforementioned graphical

inspection of stability tests that the VECM system is stable and that it is feasible to rely on

the model for inference.

viii. Short-run Modeling

Following the identification of the long term equation; the short run relationship is

estimated. This step builds on the outcomes of the weak exogeneity test that showed real

290 gross private savings per capita and the financial development indicator -m2toGDP as not weakly exogenous.

The short term equation model is constructed accordingly. The short run system is built using the co-integrating vector of real gross private savings per capita; as follows:

Drgpspc = + 0.357*Drgpspc_1 + 0.000117*DRealInterestqoq_1 + 0.018*Dm2gdpratio_1

(SE) (0.104) (0.00449) (0.048)

- 0.114*Drgpspc_2 + 0.00313*DRealInterestqoq_2 + 0.0499*Dm2gdpratio_2

(0.103) (0.00489) (0.0443)

- 0.0612*CIa_1 - 0.00577*CSeasonal + 0.00664*CSeasonal_1

(0.0254) (0.00301) (0.00672)

+ 0.00346*CSeasonal_2 - 0.0299*dumm2002q3 - 0.00293*dumm1998q4

(0.00697) (0.00905) (0.00838)

- 0.00303*dumm1999q1 + 0.0369*dumm2004q1 + 0.0409*dumm2002q4

(0.00843) (0.00851) (0.00895)

- 0.0332*dumm2008q2 + 0.00609*dumm2002q1

(0.00842) (0.00849)

Dm2gdpratio = + 0.508*Drgpspc_1 - 0.00672*DRealInterestqoq_1

(SE) (0.242) (0.0105)

- 0.00897*Dm2gdpratio_1 + 0.351*Drgpspc_2 - 0.00629*DRealInterestqoq_2

(0.112) (0.241) (0.0114)

- 0.156*Dm2gdpratio_2 + 0.176*CIa_1 - 0.134*CSeasonal

(0.104) (0.0593) (0.00704)

+ 0.000735*CSeasonal_1 - 0.0297*CSeasonal_2 - 0.0606*dumm2002q3

(0.0157) (0.0163) (0.0211)

+ 0.0104*dumm1998q4 + 0.0547*dumm1999q1 + 0.00534*dumm2004q1

(0.0196) (0.0197) (0.0199)

291 + 0.0563*dumm2002q4 - 0.0138*dumm2008q2 + 0.0653*dumm2002q1

(0.0209) (0.0197) (0.0198)

(7)

The VECM resuts in model (4) of (Annex I) indicate the unrestricted short term system of the two equations. Overall, the model is considered to be well identified. The diagnostic tests of the residuals indicate that equations of the that real gross private savings per capita and the financial development indicator -m2toGDP suffer non normality at 5 percent and 1 percent consecutively. This in turn reflected on the non- normality of the overall system’s residuals.

However, individual real gross savings per capita equations are free from hetroskedasticity and serial correlation at a 1 percent significance level. The financial development equation is free from serial correlation at a 1 percent level yet suffers hetroskedasticity at 1 percent. However, residuals diagnostics substantially improve as we proceed to the parsimonious model. Moreover, the general unrestricted short run system is reasonably stable. The roots of the companion matrix are comfortably within the unity circle to provide assurance that the data generating process is stationary and the model can be used for inference.

The speed of adjustment in the short run money demand equation is negative, statistically significant, and lies between zero and unity. Therefore it is satisfying the needed criteria to set up a robust model that converges to equilibrium in the long run. This also translates to the fact that real gross private savings per capita are above or below their long run equilibrium level.

292 Finally, the negative sign and the magnitude that is less than unity ensure that real private savings converge to the long run equilibrium. ix. Reduction of the General Model and Parsimonious Model

The estimation of the short term system with an unrestricted constant and dummies indicated that a number of variables are statistically insignificant. Since our empirical analysis reached a general statistical model that captures the key characteristics of the underlying dataset, we proceed to reducing the complexity of the general model by eliminating statistically insignificant variables and checking the validity of the reductions at every stage to ensure congruence and parsimony of the finally selected model (see

Hoover and Perez (1999) and Campos, Ericsson, and Hendry (2005)).

Throughout the reduction process, 32 regressors were eliminated based on statistical insignificance. Throughout the process, a progress test was conducted at each step to test for the validity of the elimination. This test is built on the likelihood ratio of the restricted model (after reduction) versus the unrestricted model (before reduction).

The parsimonious short term equations included some significant lagged variables in the general model in addition to the first lag of the exchange rate and US treasury bills rate.

This is in addition to contemporaneous growth in real per capita income, inflation, and

RR.

Moreover, an encompassing test was conducted to ensure that no critical information was lost during the process. The full details are provided in the annex. The parsimonious model was formulated accordingly based on minimizing the SC , HQ, and AIC information criterion, based on which it was found to encompass the general unrestricted

293 system as well as all the reduced systems. It also minimized the three information criteria.

This system reads:

Drgpspc = + 0.338*Drgpspc_1 + 0.0297*Dm2gdpratio_2 - 0.0325*CIa_1

(SE) (0.0682) (0.0109) (0.0138)

- 0.00608*CSeasonal - 0.017*DDrealPercapitaGDP - 0.224*DInflationqoq

(0.00169) (0.0101) (0.0379)

+ 0.00591*DRealInterestqoq + 0.00713*DDExchangeRate_1

(0.00274) (0.00353)

- 0.0028*DUSTBills_1 + 0.023*dumm2003q1 - 0.0215*dumm2002q3

(0.00147) (0.0064) (0.00642)

+ 0.0407*dumm2002q4 + 0.0346*dumm2004q1 + 0.0183*dumm2007q2

(0.00579) (0.00535) (0.00568)

- 0.0368*dumm2008q2

(0.00572)

Dm2gdpratio = + 0.418*Drgpspc_1 + 0.114*CIa_1 - 0.135*CSeasonal

(SE) (0.165) (0.0345) (0.00431)

- 0.00788*CSeasonal_2 - 0.0665*dumm2002q3 + 0.0575*dumm1999q1

(0.00397) (0.0143) (0.0134)

+ 0.053*dumm2002q4 + 0.0758*dumm2002q1 + 0.0711*dumm2003q1

(0.0144) (0.0136) (0.0151)

- 0.0722*dumm2007q1 + 0.046*dumm2007q2 - 0.47*DUnemployment_1

(0.0144) (0.0138) (0.255)

(8)

294 influenced the robustness of the system’s residuals failure to reject the null hypothesis of

serial correlation, hetroskedasticity, and normality tests.

Moreover, the visual inspection of the residuals diagnostics graphs, showing the plots of

actual and fitted values, cross plot of actual and fitted values, and scaled residuals, show

close fits, and the residuals aren’t seen to exhibit a pattern; despite the presence of several

outliers (see graph 37). A visual inspection of residual correlograms, residual density,

residual histogram, and residual distribution reconfirms the proposition of an absence of

serial correlation with distributions close to the normal bell shape. Moreover, the parsimonious system is remarkably stable. Visual inspection of the Chow one step test

and the break point chow test confirms this fact at a 1 percent significance level. Also the

roots companion matrix lies within unity circle. Accordingly, the model converges in the

long term. In addition to each of the four individual equations, one step residuals test

show remarkable stability within the 1 percent confidence interval (see stability test

graphs 35 and 36). This provides additional confidence to the predictive power of the

model and robustness of related findings and analysis. x. Key Economic Findings of the Short Run Parsimonious Model

The short term private savings function for Egypt is more aligned with the simple permanent income hypothesis. Savings growth is negatively related to contemporaneous

changes in income growth. The change in income growth reflects on lower growth in private savings. The change in income growth by 10 percent leads to a drop in private

savings growth by around 0.2 percent. This finding contradicts the proposition of the

Life-Cycle Model and findings of earlier studies by Modigliani, 1970; Madison, 1992;

Bosworth, 1993 and Carroll and Weil, 1994).

295 However, these findings are aligned with the simple permanent income that proposes that

current savings could drop in cases of higher future income growth. This school of

thought interprets the decline in the aggregate saving rate by the fact that the lifetime

wealth of the young is high enough relative to that of their elders (Athukorala and Sen,

2004). This in turn reflects on lower rates of inter-temporal substitution and triggers

higher preference to current consumption. Accordingly, growth in per capita income

could actually lead to a decrease in saving. This finding provides significant guidance to policy on the need to provide robust involuntary savings mechanisms. This is critical in

order to smooth consumption by the young across time and mitigate any incidence of

future poverty among the elderly.

Macroeconomic uncertainty negatively influences private savings. Despite the fact that

this contradicts the theory of precautionary savings, it is a common finding in empirical

studies on savings behavior. Loayza, Schmidt-Hebbel, and Servén (2000) find only one

of the six seminal empirical studies on savings and growth in less developed countries

that include inflation among the explanatory variables and finds a positive and significant

effect on the private saving rate. This is interpreted by the fact that macroeconomic

uncertainty in less developed countries —suffering financial market imperfection—leads

to greater reliance on non financial inflation hedges, such as investments in real estate

and gold, that are perfect substitutes for financial savings.

Short term influence of real interest rates and financial development on real private

savings is in line with theory and our earlier interpretation of the long term equation.

Both variables are positively related to real private savings in the short term. This is in

full conformity with the LCH.

296 Market openness has a significant effect on private savings. Higher devaluation of the

foreign exchange rate in the previous period leads to higher real private savings. This is

consistent with the analysis on the impact of short term inflation. Devaluation of

domestic currency in less developed countries with limited capital mobility raises the public aversion to future uncertainties and triggers higher precautionary savings to

mitigate future macroeconomic shocks. Another interpretation is that devaluation inflates

the volume of the equivalence of foreign currency deposits in domestic currency. This in

turn leads to a positive net effect for exchange rate devaluation on real private savings.

Furthermore, the rise in the foreign exchange rate triggers dis-savings and a preference to

store value in non financial assets (See Mohieldin 1995).

7. Conclusion

Real private savings behavior in Egypt is influenced by a mix of simple permanent

income and life cycle hypotheses determinants. This study provides pioneering an

empirical investigation on savings behavior in Egypt using high quality and high

frequency quarterly data spanning the period 1991–2010 and employing a robust VECM

strategy. The key long term determinants of private savings in Egypt are RR and level of

financial development. Data also shows a higher persistence of real private savings in the

short run.

RR varies negatively with real private savings in the long term as it triggers higher

consumption at lower cost. However, in the short term, real private savings vary positively with RR, whereas, financial development reflects positively on higher private

297 savings on both horizons. This is a literal translation of the Mckinnon -Shaw hypothesis on financial development and growth that strongly holds in the case of Egypt.

The effect of higher inflation on real savings contradicts theory and negates the presence of a precautionary motive to saving in Egypt. This can be attributed to seeking alternative non-formal inflation hedges, particularly in real estate and gold. This is common in less developed countries that suffer financial market imperfections. Unavailability of robust data on real estate holdings and gold prices in the domestic market limits the interpretation of this result and raises a feasible research question for any future development of this model.

Higher devaluation reflects on higher private savings. The devaluation of the exchange rate provides signals of macroeconomic uncertainty to economic agents and triggers a precautionary motive for savings. In the absence of data on domestic and foreign currency denominated private savings, devaluation would reflect on raising the domestic currency equivalence of foreign currency savings, leading to a net positive effect of devaluation on private savings.

Finally, more future work is needed as accurate and high frequency data on government debt and government deposits are made available to further test for the Ricardian-

Equivalence and the effect of fiscal policies on private savings .0T

In general, macroeconomic policies heavily influence savings behavior in particular and resource mobilization in Egypt. Robust macroeconomic and monetary policies are prerequisites to maximize private savings and financing growth in Egypt.

298 References

1. Agrawal, P., (2001), The Relation between Savings and Growth: Cointegration and Causality Evidence from Asia, Applied Economics, 33, pp. 499-513. 2. Agrawal, P. and P., Sahoo, (2009), Saving and Growth in Bangladesh, The Journal of Developing Areas, 42, Iss. 2, 89-110. 3. Al-Mashat, R. A., (2006), Financial Sector Development and Economic Growth in Egypt 1960-1999, paper presented as part of the Global Research Project (GRP) conducted by the Economic Research Forum, available at http://www.erf.org.eg/html/grp_list%202.asp 4. Ando, A., and F., Modigliani, (1963), The ‘life-cycle’ hypothesis of saving: aggregate Implications and tests, American Economic Review, 53(1), 55–84. 5. Arrieta, G. (1988), Interest Rates, Savings and Growth in LDCs: An Assessment of Recent Empirical Research, World Development, 16: 589-605. 6. Athukorala, P and Sen, K., (2004), The determinants of private saving in India, World Development, vol. 32, no. 3, pp. 491-503. 7. Bahmani-Oskooee, M., S., Hegerty, H., Wilmeth, (2012), The Saving-Investment Gap and Income Inequality: Evidence from 16 Countries, The Journal of Developing Areas, Journal of Developing Areas College of Business, Tennessee State University. 8. Benston, G., and G. Kaufman, (1990), Understanding the savings-and loan debacle, The Public Interest 99 (April):79-95. 9. Bewley, F., (1977), The Permanent Income Hypothesis: A Theoretical Formulation, J. Econ. Theory 16: 252–92. 10. Burnside, C. and D., Dollar (2000), Aid, Policies, and Growth, American Economic Review 90(4):847–68. 11. Campbell, J.Y., Perron, P., (1991), Pitfalls and opportunities: What macroeconomists should know about unit roots, National Bureau of Economic Research. In: Macroeconomics Annual. MIT Press, Cambridge, MA, pp. 141–201. 12. Campos, J., N. R. Ericsson and D. F. Hendry (2005), “Editors’ Introduction”, in: Campos, J., N. R. Ericsson and D. F. Hendry (eds.), Readings on General-to- Specific Modeling, Cheltenham: Edward, Elgar, 1–81. 13. Carroll, C., (1991), Buffer Stock Saving and the Permanent Income Hypothesis, Working Paper Series 114. Board of Governors of the Federal Reserve System, Economic Activity Section, Washington, D.C. 14. Carroll, C., and A., Samwick, (1995), How Important Is Precautionary Saving? NBER Working Paper 5194. National Bureau of Economic Research, Cambridge, Mass. 15. Chow, G. C. and A., Lin, (1971). Best linear unbiased interpolation, distribution, and extrapolation of time series by related series, The Review of Economics and Statistics, 53(4): 372 – 75. 16. Cotis, J. and J. Coppe (2005), Business Cycle Dynamics in OECD Countries: Evidence, Causes and Policy Implications, A working paper presented to Reserve Bank of Australia Economic Conference on The Changing Nature of the Business Cycle, Sydney, Australia – July 2005.

299 17. Davidson R. and J. MacKinnon (1999), Econometric Theory and Methods. Oxford University Press. 18. Deaton, A., (1991), Saving and Liquidity Constraints. Econometrica 59(July):1121– 42. 19. Dhanasekaran, K., Testing of Savings-Growth Relationship in India: An Application of Cointegration and Error Correction Techniques (June 18, 2010). The IUP Journal of Applied Economics, Vol. 9, No. 3, pp. 85-96, 2010. 20. Dickey, D. and W. Fuller (1979), Distribution of the Estimators for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Association, 74, 427-431. 21. ElBassam, K., (2005), A Model of House Hold Saving in Saudi Arabia:An Empirical Study of the Period 1970-2000, Journal of King Abdulaziz University : Economics and Administration, 01/2005. 22. Elsayed, H., (1993), Determinants of Savings in Egypt, Egypt Contemperaire, Vol. 421, pp. 127 – 156. 23. Enders, W., (1995), Applied Econometric Time Series, New York, John Wiley and Sons Inc. 24. Engle, R.F. and C.W.J. Granger, (1987), Co-integration and error correction: representation, estimation and testing, Econometrica, Vol. 55, No. 2. (Mar., 1987), pp. 251-276. 25. Friedman, A., (1957), A Theory of the Consumption Function, Princeton University Press. 26. Gale, G., and J., Sabelhaus, (1999), Perspectives on the Household Saving Rate, Brookings Papers on Economic Activity. 27. Giavazzi, F., T., Jappelli, and M., Pagano, (2000), Searching for Non-Linear Effects of Fiscal Policy: Evidence From Industrial and Developing Countries, European Economic Review, vol. 44, no. 7, pp. 1259–1289. 28. Granger, J., (1983), Cointegrated Variables and Error Correction Models, UCSD Discussion paper, 83-13a. 29. Gupta, L., (1987), Aggregate Savings, Financial Intermediation, and Interest Rate, Review of Economics and Statistics, pp. 303-311. 30. Hevia, C., & N., Loayza, (2012), Saving and Growth in Egypt, Middle East Development Journal (MEDJ), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1250002-1-1. 31. Hoover, K. D. and Perez, S. J. (1999). Data mining reconsidered: Encompassing and the general-to-specific approach to specification search, Econometrics Journal, Vol. 2, pp. 1–25. 32. Hussein, K. A., c. (2002), Finance and Growth in Egypt, presented at the conference organized by EPIC and ECES, 24th July 1999, Cairo,

www.iceg.org/NE/projects/financial/growth.pdf37TU U37T 33. Keynes, M., (1936), The General Theory of Employment, Interest, and Money, New York, Harcourt Brace and World. 34. Kim, M., (2010), The Determinants Of Personal Saving In The U.S., Journal of Applied Business Research, Vol 26, No 5.

300 35. King, A., (1985). The Economics of Saving: A Survey of Recent Contributions, Kenneth J. Arrow and Seppo Konkapohja, editors, Frontiers in Economics, Basil Blackwell. 36. Koskela, E. and M., Viren, (1982), Saving and inflation: some international evidence, Economics Letters, 9(4). 37. Kwakwa, P., (2013), Determinants of National Savings: A Short and Long Run Investigation in Ghana, Journal of Business and Management, Vol. 5 Issue No. 1. 38. Loayza, N., and R., Shankar, (2000), Private Saving in India, The World Bank Economic Review, VOL. 14, NO. 3. 39. Loayza, N., K., Schmidt-Hebbel, and L., Serven, L., (2000), What drives private savings across the World, The Review of Economics and Statistics, May, 165–81. 40. Love, I., (2011), Empirical analysis of corporate savings in Egypt, Policy Research Working Paper Series 5634, The World Bank. 41. Modigliani, F., and A. Ando, (1957), Tests of the Life Cycle Hypothesis of Savings: Comments and Suggestions, Bulletin of the Oxford University Institute of Statistics. 42. Modigliani, F., and R., Brumberg, (1954), Utility Analysis and the Consumption Function, In K. K. Kurihara, ed., Post-Keynesian Economics. New Brunswick, N.J.: Rutgers University Press. 43. Modigliani, F., (1998), The role of intergenerational transfers and life-cycle saving in the accumulation of wealth, Journal of Economic Perspectives, 2(2), 15–20. 44. Mohieldin, M., (1995), On financial liberalisation in LDCs : the case of Egypt, 1960-93. PhD thesis, University of Warwick, United Kingdom. 45. Muradoglu, G. and F., Taskin, (1996), Differences in household savings behavior: Evidence from industrial and developing countries, The Developing Economies 34(2), 138-153. 46. Nordhaus, D., (1996), Budget Deficits and National Saving, Challenge, (March - April), 45-49. 47. Nwachukwu, T., and F., Egwaikhide, (2007), An Error-Correction Model of the Determinants of Private Saving in Nigeria, A Paper presented at the African Economic Society (AES), Conference, Cape Town, South Africa. 48. Ortmeyer, D., (1985), A Portfolio Model of Korean Household Saving Behavior, 1962-1976, Economic Development and Cultural Change, vol. 33, no. 3, pp. 575- 599. 49. Paxson, C., H., (1996), Saving and growth: evidence from micro data, European Economic Review, 40, 255–88. 50. Pesaran, M., H., and Y., Shin, (1999), An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis, in S Strom, (ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium, Cambridge: Cambridge U P. 51. Pesaran, M., H., Y., Shin, and R., J., Smith, (2001), Bounds Testing Approaches to the Analysis of Level Relationships, Journal of Applied Econometrics, 16, 289-326. 52. Poterba, J., M., (1994), Public policies and household saving, Chicago: University of Chicago Press. 53. Schmidt-Hebbel, K. and L., Servén, (1997), Saving across the World: Puzzles and Policies, World Bank Discussion Papers 354 (Washington: The World Bank).

301 54. Schmidt-Hebbel, K. and L., Servén, (2000), Does Income Inequality Raise Aggregate Saving? Journal of Development Economics, Vol. 61, pp. 417-446. 55. Schwert, W., (1989), Tests for unit roots: A Monte Carlo investigation, Journal of Business and Economic Statistics, 7 (1989), pp. 147–159. 56. Skinner, J., (1988), Risky Income, Life-Cycle Consumption, and Precautionary Savings, Journal of Monetary Economics 22(2):237–55. 57. Slami, A., and M., Sheikhy, (2008), Estimating Algeria’s Savings Function 1970- 2005, Journal of Social Research, University of Algeria, issue 6, pp. 129 -144. 58. Tahir, J., (1995), Recent Developments in Demand for Money Issues: Survey of Theory and Evidence with Reference to Arab Countries. Working Paper 9530, Arab Planning Institute. 59. Touny, M., (2008), Determinants of Domestic Savings Performance in Egypt: An Empirical Study, Journal of Commercial Studies and Researches, Faculty of Commerce, Benha University. 60. Awan, R. U., Munir, R., Hussain, Z., & Sher, F., (2010), Rate of Interest, Financial Liberalization & Domestic Savings Behavior in Pakistan, International Journal of Economics and Finance, 2(4), p75. 61. Zeldes, S., (1989), Consumption and Liquidity Constraints: An Empirical Investigation, Journal of Political Economy 97(2):305–46.

302

Annex XIX: Key Findings of Existing Empirical Literature on Savings

Behavior in Egypt

303 Table (28): Summary of Findings of the Research on Savings Behavior in Egypt:

Data Properties Modeling Approach

Sample and Frequency Type Source Variables Integration Specification Estimates

1969-1990 Annual Domestic (not national) Stepwise Regress real per EquationU 1: savings; private savings regression capita domestic El Sayed (1993) (income less and savings on Real per capita domestic consumption); Multiple permanent income savings = 59.435 + regression per capita, 0.237*permanent income per Private income (GDP less temporary capita + 0.931*temporary government revenue plus income, expected income + 3.976*expected direct and indirect inflation, inflation – transfers such as pensions unemployment. 7.872*Unemployment. and social insurance); GDP Deflator (to compute income variables 2 in real terms); real savings RP P = 0.81; F = 16.87 ; D.W. per capita. = 1.54 ; no. observations = 21.

Exogenous variables: real

income per capita; EquationU 2: inflation rate (actual and expected); per capita (ln) real per capita private liquidity (Money supply, savings = 7.91 + 1.951*(ln) quasi money and savings Include ToT real per capita available deposits); nominal and income – 0.836*(ln) dummy real interest rate on one- – 0.282*(ln) actual inflation year deposits; + 0.395*(ln) expected unemployment rate; inflation – 0.606*(ln) exchange rate; and nominal interest rate - dummy variable (to 0.421*(ln) Unemployment + differentiate between 0.465*(ln) ToT. years pre and post infitah , or economic

304 Table (28): Summary of Findings of the Research on Savings Behavior in Egypt:

Data Properties Modeling Approach

Sample and Frequency Type Source Variables Integration Specification Estimates

liberalization/openness).

2 RP P = 0.97; F = 30.6 ; no. observations = 15. Differentiation between Permanent versus temporary income.

EquationU 3:

(ln) real per capita private Expected inflation is savings = 0.0003 + 2.570 * obtained by regressing (ln) real per capita available actual inflation rate on a Include Per income - 0.722*(ln) Dummy time variable. Capita Money - 0.618*(ln) per capita money Balances balances.

2 RP P = 0.74; F =10.42; D.W. = 1.76; no. observations = 15.

1967 – 1996 Annual For Financial I (1) ECM Regress the ratio The long run relationship development: (unrestricte of financial suggests that 1% increase in Hussein (1999) d, then savings/GDP, sy , y leads to 0.61 to 0.75% a) Ratio of financial restricted to on real per capita increase in financial savings. savings/GDP. two lags) income, y, the b) ratio of bank claims on growth private sector to GDP; cpy . rate of real GDP, In the short run, a 1% rise in c) ratio of bank claims to g, the expected

305 Table (28): Summary of Findings of the Research on Savings Behavior in Egypt:

Data Properties Modeling Approach

Sample and Frequency Type Source Variables Integration Specification Estimates

the private sector to the real interest rate, interest rate causes an total domestic credit; re , and expected increase in financial savings cpc. inflation rate, πe . by 0.04%. d) ratio of deposit money banks (DMB) domestic assets to DMB domestic assets plus central bank domestic assets; bank.

1978-1999 Annual Non-government savings I (1) First Regress non Statistically significant rate, regressed on the real difference is government results: Al-Mashat (2002) interest rate, the ratio of implemente savings rate on M2 to GDP, credit to the d to all data Real, M2/GDP (or Whole sample 1978-1999: non-government sector in series M2D), CPD, percent of GDP, and the RMQM, RMTD, Reserve money to deposits ratio of reserve money to plus OPEN and ratio increase by 1% lead to a quasi money and to total DUMMY and decline of non government deposits, and finally a Dependency. savings by 0.528, cetris measures of the level of paribas financial development: (i) Plus Depth, Bank, Private, and Bank DEPTH, measures the credit . size of financial Subsample 1978-1990: intermediaries and equals liquid liabilities of the Credit to private sector increase by 1% lead to a financial system (currency decline in non government plus demand and interest- savings by 0.933, cetris bearing liabilities of paribas banks and nonbank financial intermediaries)

306 Table (28): Summary of Findings of the Research on Savings Behavior in Egypt:

Data Properties Modeling Approach

Sample and Frequency Type Source Variables Integration Specification Estimates

divided by GDP, (ii) BANK, measures the degree to which the Subsample 1991-1999: central bank versus commercial banks are Data suggest suggests that allocating credit and 1% increase in real interest equals the ratio of bank rate leads to a decline in non credit divided by bank government savings by credit plus central bank 0.010, cetris paribas domestic assets, (iii) PRIVATE equals the ratio of credit allocated to private enterprises to total Credit to private sector domestic credit increase by 1% lead to a (excluding credit to decline in non government banks) (iv) PRIVY equals savings by 2.844, cetris credit to private paribas enterprises divided by GDP.

(v) BANKCREDIT is the Reserve money to quasy value of loans made by money increase by 1% lead commercial to a decline of non government savings by 1.62 banks and other deposit- taking banks to the private sector divided by GDP .

307 Table (28): Summary of Findings of the Research on Savings Behavior in Egypt:

Data Properties Modeling Approach

Sample and Frequency Type Source Variables Integration Specification Estimates

1975-2006 Annual Domestic savings to GDP I(1) ECM. Regress Domestic ShortU run results: (DS); growth rate of fixed savings to GDP Touny (2008) per capita income (GPCI); (DS); on growth budget deficit ratio to rate of fixed per ∆ = −11.99 + 0.17229 GDP (BD); real interest capita income ∗ ∆ rate (RR); ratio of broad (GPCI); budget − 0.26858 money supply or M2 to deficit ratio to ∗ ∆ GDP (M); inflation rate GDP (BD); real − 0.03537 (INF); current account interest rate (RR); deficit ratio to GDP (CA); ratio of broad ∗ ∆ money supply or + 2.86926 M2 to GDP (M); ∗ ∆ inflation rate − 0.28001 ∗ ∆ (INF); current account deficit + 2.74281 ∗ ∆ ratio to GDP (CA);

Long-runU Results:

∆DS RtR = 0.60062*GPCI Rt-1R –

0.25645*BD Rt-1R +

0.25768*M Rt-1R -0.43422*CA Rt-

1R + 0.29818*RR Rt-1R -

0.80505*DS Rt-1R -0.93547* u Rt- 1

2 ; 2 RP =P 0.8585 P Adj.P R P =P 0.7502; D.W. = 2.933; F

308 Table (28): Summary of Findings of the Research on Savings Behavior in Egypt:

Data Properties Modeling Approach

Sample and Frequency Type Source Variables Integration Specification Estimates

=7.93; no. obs. = 31.

309

Annex26T XX: Summary Tables and Integration Tests for Savings

Behavior Model 26T

310 Table (29): Data Description for Savings Behavior Model:

Acronym Description Source Adjusted Date Accessed

Yes Egyptian Central (log of levels of Log of real gross private quarterly th rgpspc Authority for March 30 P P 2015 savings per capita Mobilization interpolated data and Statistics from annual series using Chow Lin disaggregation )

Quarterly Series Yes Obtained from Growth (First Egyptian (first difference of th DrealPercapitaGDP Difference) of Logs) of March 30 P P 2015 Cabinet’s real per capita GDP log of levels Information deflated by CPI: and Decision base year 2010) Support Center

IMF Inflation Rate: quarter International th Inflationqoq on quarter change in No March 30 P P 2015 Financial CPI Statistics

Real Interest Rate: IMF Three months deposit International th RealInterestqoq rate less inflation No March 30 P P 2015 Financial (quarter on quarter Statistics inflation)

IMF Financial Development International th m2gdpratio Indicator: log of M2 to No March 30 P P 2015 Financial GDP ratio Statistics

Expected Exchange IMF Yes Rate Depreciation: of International th DExchangeRate differenced log of March 30 P P 2015 Financial official nominal (First Difference of Statistics exchange rate levels)

IMF Foreign Interest Rate: International th USTBills No March 30 P P 2015 US Treasury Bills Rate Financial Statistics

311 Table (30): Summary Statistics:

Sample: 1993(quarter: 1) - 2010(quarter: 4) (72 observations; qurterly data covering 18 years)

# of Variables: 7

Variable Leading #obs #miss Minimum Mean Maximum std.dev sample

1993(1) - 72 0 0.46401 0.61723 0.78112 0.11191 rgpspc 2010(4)

DrealPercapitaG 1993(1) - 72 0 -0.097638 0.0086462 0.15028 0.044196 DP 2010(4)

1993(1) - 72 0 0.00000 0.018889 0.080000 0.016746 Inflationqoq 2010(4)

1993(1) - 72 0 5.8600 8.6786 11.990 1.9372 RealInterestqoq 2010(4)

1993(1) - 72 0 -0.37106 -0.20299 -0.030459 0.091823 m2gdpratio 2010(4)

1993(1) - 72 0 -0.34000 0.036806 1.2300 0.17118 DExchangeRate 2010(4)

1993(1) - 72 0 0.050000 3.3743 6.2200 1.9594 USTBills 2010(4)

Table (31): Augmented Dickey-Fuller Tests for Unit Roots: Second Differences of Log Levels of Real Data - Sample 1993(2) - 2010(4)

Variable t-adf beta Y_lag t-DY_lag Maximum Lags AIC

DDrgpspc -8.330** -2.4482 3.274 3 -8.925

DDDrealPercapitaG -9.920** -14.573 4.033 6 -6.243 DP

DDInflationqoq -7.075** -6.4815 2.577 6 -7.911

DDRealInterestqoq -5.640** -2.3274 1.654 5 -2.610

DDm2gdpratio -5.869** -4.4654 1.546 6 -7.233

T=73, Constant+Trend+Seasonals; 5%=-3.47 1%=-4.09

P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

312 Table (32): Augmented Dickey-Fuller Tests for Unit Roots: First Differences of Log Levels of Real Data - Sample 1993(1) - 2010(4)

Variable t-adf beta Y_lag t-DY_lag Maximum Lags AIC

Drgpspc -7.254*** 0.11175 0 -8.990

DDrealPercapitaGD -10.05*** -1.0780 3.083 1 -5.740 P

DInflationqoq -7.873*** -1.6105 2.790 3 -8.237

DRealInterestqoq -5.200*** 0.29126 1.555 1 -2.835

Dm2gdpratio -7.952*** 0.028625 0 -7.345

T=73, Constant+Trend+Seasonals; 5%=-3.47 1%=-4.09

P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

Table (33): Augmented Dickey-Fuller Tests for Unit Roots: Levels of Real Data - Sample 1993(1) - 2010(4)

Variable t-adf beta Y_lag t-DY_lag Maximum Lags AIC

rgpspc -1.824 0.93194 2.724 5 -9.018

DrealPercapitaGDP -10.49** -1.6168 4.614 2 -6.720

Inflationqoq -5.934** 0.26828 2.500 1 -8.329

RealInterestqoq -2.658 0.86223 4.333 1 -2.911

m2gdpratio -0.8490 0.96645 0 -7.356

T=73, Constant+Trend+Seasonals; 5%=-3.47 1%=-4.09

P-values are in brackets;

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

313

Annex XXI: Diagnostic Tables for Savings Behavior Model

314 Table (34): Baseline Money Demand Model Lag Structure and Reduction Tests, Sample 1959 - 2013

Significance of Each Lag Joint AIC SC F Test significance of all lags Restricted Lags 6 5 4 3 2 1

0.48752 0.43503 1.2811 0.97507 1.2992 29.488 0.48752 6 -11.62 -8.92 F(9,104) [0.8801] [0.9133] [0.2560] [0.4650] [0.2463] [0.0000]** [0.8801]

0.90839 0.90839 1.4299 1.2372 1.6254 32.584 5 -11.77 -9.34 F(9,112) [0.5207] [0.1837] [0.2797] [0.1164] [0.0000]** [0.5207]

2.2159 1.1486 1.9124 39.909 2.2159 4 -11.84 -9.69 F(9,119) [0.0255]* [0.3345] [0.0564] [0.0000]** [0.0255]*

1.0402 2.0949 41.196 1.0402 - - 3 F(9,126) [0.4120] [0.0346]* [0.0000]** [0.4120] 11.7120 9.84389

3.2725 46.703 3.2725 - - 2 F(9,134) [0.0012]** [0.0000]** [0.0012]** 11.7818 10.2338

818.78 818.78 - - 1 F(9,141) [0.0000]** [0.0000]** 11.5415 10.2338

P-values are in brackets ; *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

The Savings Model VAR System is presented as follows:

, rgpspc rgpspc DrealPercapitaGDP DrealPercapitaGDP , , Inflationqoq . Inflationqoq = + ∑ dumm1998q4 + RealInterestqoq RealInterestqoq , m2gdpratio m2gdpratio , dumm1999q1 + dumm2002q1 + dumm2002q3 ++ Centered

Seasonaldumm2002q4 Dummies ++ dumm2004q1 + dumm2008q2 Where: rgpspc, realPercapitaGDP, m2gdpratio are in Logs dummXXXX: dummy variable (year/quarter)

: error term

315 Table (35): Tests on the significance of each variable

Variable F-test Value [ Prob]

rgpspc F(18,122) = 8.2487 [0.0000]***

RealInterestqoq F(18,122) = 9.4120 [0.0000]***

m2gdpratio F(18,122) = 5.0142 [0.0000]***

dumm2002q3 F(3,43) = 5.5426 [0.0026]***

dumm1998q4 F(3,43) = 0.59090 [0.6243]

dumm1999q1 F(3,43) = 5.2644 [0.0035]***

dumm2004q1 F(3,43) = 3.6965 [0.0188]**

dumm2002q4 F(3,43) = 4.2020 [0.0108]**

dumm2008q2 F(3,43) = 5.1064 [0.0041]***

dumm2002q1 F(3,43) = 3.3207 [0.0285]**

Constant F(3,43) = 4.6021 [0.0070]***

CSeasonal F(9,104) = 3.5984 [0.0006]***

P-values are in brackets

*** Significance level at 1%; ** Significance level at 5%; * Significance level at 10

316 Table (36): Residual Diagnostics Tests for the – 3 Lags VAR- Savings Behavior Model:

Single-equation diagnostics using reduced-form residuals: rgpspc : Portmanteau( 8): Chi^2(5) = 7.9407 [0.1595] rgpspc : AR 1-5 test: F(5,49) = 0.45003 [0.8112] rgpspc : ARCH 1-4 test: F(4,66) = 0.36262 [0.8344] rgpspc : Normality test: Chi^2(2) = 3.8044 [0.1492] rgpspc : Hetero test: F(21,45) = 1.0481 [0.4323] rgpspc : Hetero-X test: not enough observations

RealInterestqoq: Portmanteau( 8): Chi^2(5) = 1.0482 [0.9586]

RealInterestqoq: AR 1-5 test: F(5,49) = 0.13964 [0.9822]

RealInterestqoq: ARCH 1-4 test: F(4,66) = 0.82334 [0.5150]

RealInterestqoq: Normality test: Chi^2(2) = 2.1719 [0.3376]

RealInterestqoq: Hetero test: F(21,45) = 0.69936 [0.8110]

RealInterestqoq: Hetero-X test: not enough observations m2gdpratio : Portmanteau( 8): Chi^2(5) = 6.9724 [0.2227] m2gdpratio : AR 1-5 test: F(5,49) = 1.4266 [0.2314] m2gdpratio : ARCH 1-4 test: F(4,66) = 0.68421 [0.6054] m2gdpratio : Normality test: Chi^2(2) = 10.459 [0.0054]** m2gdpratio : Hetero test: F(21,45) = 1.0826 [0.3985] m2gdpratio : Hetero-X test: not enough observations

Vector Portmanteau( 8): Chi^2(45) = 67.973 [0.0151]*

Vector AR 1-5 test: F(45,110) = 1.3605 [0.0993]

Vector Normality test: Chi^2(6) = 13.314 [0.0383]*

Vector Hetero test: F(126,239)= 1.0170 [0.4505]

Hetero-X test: not enough observations

Vector RESET23 test: F(18,130) = 1.8696 [0.0238]*

317

Table 37(a): Baseline – 3 Lags VAR- Savings Behavior Model:

Cointegration Analysis with Johansen Test: sample 1959–2013

eigenvalue Log likihood Trace test Max test Trace test Max test rank for rank [ Prob] [ Prob] (T-nm) (T-nm)

44.98 29.52 39.51 25.93 0 470.8575 [0.015]** [0.012]** [0.003]*** [0.003]***

15.46 12.54 13.58 11.01 1 0.32895 485.6172 [0.327] [0.260] [0.205] [0.161]

2.92 2.92 2.57 2.57 2 0.15582 491.8848 [0.603] [0.602] [0.669] [0.668]

3 0.038718 493.3458

P-values are in brackets, *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

Table 37(b): Eigen vector of the variables entering into the respective cointegrating vector beta (scaled on diagonal; cointegrating vectors in columns) rgpspc 1.0000 19.041 0.87189

RealInterestqoq 0.045789 1.0000 0.028402 m2gdpratio -0.39439 -5.4924 1.0000

Constant -1.1301 -21.190 0.98448

alpha rgpspc -0.066459 0.00066369 -0.029979

RealInterestqoq -0.47952 -0.14404 -0.28516 m2gdpratio 0.19591 0.0035570 0.060292

Constant -0.066459 0.00066369 -0.029979

318

Table 37(c): Reduced Rank Standardized Coefficients

Beta Vector Std Err Alpha Vector Std Err rgpspc 1.0000 0.00000 -0.059411 0.024957

RealInterestqoq 0.045087 0.0044744 0.00000 0.00000 m2gdpratio -0.40821 0.11153 0.17236 0.058731

Constant -1.1317 0.033245 -0.059411 0.024957

Table 37(d): Hypotheses Tests for the Beta Vector rgpspc Zero Chi^2(1) 6.5250 [0.0106]**

RealInterestqoq Zero Chi^2(1) 6.1389 [0.0132]** m2gdpratio Zero Chi^2(1) 4.2500 [0.0393]**

Constant Zero Chi^2(1) 6.7915 [0.0092]***

Joint Significance for betas (including Zero Chi^2(3) 9.6325 [0.0220]** constant)

Table 37(e): Hypotheses Tests for the Alpha Vector: Weak Exogeneity and Joint Tests rgpspc Zero Chi^2(1) 6.4004 [0.0114]**

RealInterestqoq Zero Chi^2(1) 0.28427 [0.5939] m2gdpratio Zero Chi^2(1) 9.2625 [0.0023]**

Joint Significance for alphas of ( RealInterestqoq Zero Chi^2(2) 6.4985 [0.0388]** rgpspc)

P-values are in brackets, *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10%

319 Table (38): Parsimonious/Reduced VECM:

MOD( 6) Estimating the model by FIML The dataset is: C:\Users\ahmedrostom\Dropbox\Data\Chapter 3\July 2015 Modeling\EGYSAV1990Q12010Q4.in7 The estimation sample is: 1992(3) - 2010(4) Equation for: Drgpspc Coefficient Std.Error t-value t-prob Drgpspc_1 0.337650 0.06824 4.95 0.0078 Dm2gdpratio_2 0.0297050 0.01087 2.73 0.0523 CIa_1 -0.0325256 0.01384 -2.35 0.0785 CSeasonal -0.00608295 0.001690 -3.60 0.0228 DDrealPercapitaGDP -0.0169958 0.01008 -1.69 0.1670 DInflationqoq -0.223501 0.03787 -5.90 0.0041 DRealInterestqoq 0.00591453 0.002737 2.16 0.0968 DDExchangeRate_1 0.00713261 0.003532 2.02 0.1136 DUSTBills_1 -0.00280132 0.001471 -1.90 0.1296 dumm2003q1 0.0230027 0.006402 3.59 0.0229 dumm2002q3 -0.0215290 0.006420 -3.35 0.0285 dumm2002q4 0.0406508 0.005793 7.02 0.0022 dumm2004q1 0.0346210 0.005347 6.48 0.0029 dumm2007q2 0.0183320 0.005677 3.23 0.0320 dumm2008q2 -0.0367877 0.005723 -6.43 0.0030 sigma = 0.00537313

Equation for: Dm2gdpratio Coefficient Std.Error t-value t-prob Drgpspc_1 0.418215 0.1645 2.54 0.0639 DUnemployment_1 -0.470442 0.2554 -1.84 0.1393 CIa_1 0.114179 0.03452 3.31 0.0297 CSeasonal -0.134988 0.004307 -31.3 0.0000 CSeasonal_2 -0.00787765 0.003973 -1.98 0.1184 dumm2002q3 -0.0664684 0.01428 -4.65 0.0096 dumm1999q1 0.0575097 0.01344 4.28 0.0129 dumm2002q4 0.0529795 0.01439 3.68 0.0212 dumm2002q1 0.0757993 0.01360 5.57 0.0051 dumm2003q1 0.0711083 0.01511 4.70 0.0093 dumm2007q1 -0.0722214 0.01444 -5.00 0.0075 dumm2007q2 0.0460144 0.01381 3.33 0.0290 sigma = 0.0136647 log-likelihood 513.271188 -T/2log|Omega| 723.274091 no. of observations 74 no. of parameters 27

LR test of over-identifying restrictions: Chi^2(113)= 378.34 [0.0000]** BFGS using analytical derivatives (eps1=0.0001; eps2=0.005): Strong convergence correlation of structural residuals (standard deviations on diagonal) Drgpspc Dm2gdpratio Drgpspc 0.0053731 0.34019 Dm2gdpratio 0.34019 0.013665

Single-equation diagnostics using reduced-form residuals: Drgpspc : AR 1-1 test: F(1,58) = 1.3775 [0.2453] Drgpspc : ARCH 1-1 test: F(1,72) = 0.025170 [0.8744] Drgpspc : Normality test: Chi^2(2) = 2.4213 [0.2980] Drgpspc : Hetero test: F(17,52) = 0.78482 [0.7014] Dm2gdpratio : AR 1-1 test: F(1,61) = 2.0273 [0.1596] Dm2gdpratio : ARCH 1-1 test: F(1,72) =0.0002804 [0.9867] Dm2gdpratio : Normality test: Chi^2(2) = 4.6519 [0.0977] Dm2gdpratio : Hetero test: F(8,61) = 0.67928 [0.7079] Vector SEM-AR 1-1 test: F(4,116) = 0.70737 [0.5885] Vector Normality test: Chi^2(4) = 8.8177 [0.0658]

320

Annex XXII: Graphical Representation and Diagnostics for Savings

Behavior Model

321 Graph (32): Structure of Aggregate Private Savings in Egypt:

100%

90%

80%

70%

60%

50%

40%

30%

20%

Institutional 10% Banking 0%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Source: Central Bank of Egypt and Central Authority for Public Mobilization and Statistics of Egypt, National Savings Survey 2014

322 Graph (33a): Plot of Log of Levels and First Differences of Logs of Variables for Savings Behavior Model: 2015 2015 2015 2015 2015 2010 2010 2010 2010 2010 2005 2005 2005 2005 2005 2000 2000 2000 2000 2000 1995 1995 1995 1995 1995 Drgpspc Drgpspc DRealInterestqoq Dm2gdpratio DDrealPercapitaGDP DInflationqoq 1990 1990 1990 1990 1990 0.5 0.1 0.2 -0.5 -0.1 -0.2 0.05 -0.05 0.025 -0.025 2015 2015 2015 2015 2015 2010 2010 2010 2010 2010 2005 2005 2005 2005 2005 2000 2000 2000 2000 2000 1995 1995 1995 1995 1995 rgpspc rgpspc RealInterestqoq m2gdpratio DrealPercapitaGDP Inflationqoq 1990 1990 1990 1990 1990 0.7 0.5 7.5 0.2 0.0 -0.1 -0.3 12.5 0.075 0.025

323 Graph (33b): Diagnostic Graphs of - 3 Lags VAR - Savings Behavior Model: 10 10 10 5 5 5 ACF-r:rgpspc ACF-r:RealInterestqoq ACF-r:m2gdpratio 0 0 0 1 0 1 0 1 0 5.0 N(0,1) 2.5 2.5 N(0,1) 2.5 N(0,1) 0.0 0.0 0.0 r:rgpspc r:RealInterestqoq r:m2gdpratio -2.5 -2.5 Density Density Density -2.5 0.4 0.2 0.75 0.50 0.25 0.75 0.50 0.25 2010 2010 2010 2000 2000 2000 r:rgpspc(scaled) r:RealInterestqoq(scaled) r:m2gdpratio (scaled) 2 0 2 0 -2 -2 2.5 0.0 2010 2010 2010 Fitted Fitted Fitted 2000 2000 2000 rgpspc RealInterestqoq m2gdpratio 0.8 0.7 0.6 0.5 7.5 0.0 -0.1 -0.2 -0.3 10.0

324 Graph (34a): Scaled Residuals - 3 Lags VAR- for Savings Behavior Model:

r rgpspc 0.025

0.000

-0.025

1995 2000 2005 2010

1 r RealInterestqoq

-1 1995 2000 2005 2010

0.05 r m2gdpratio

0.00

-0.05 1995 2000 2005 2010

Graph (34b): Long Run Stability Test of - 3 Lags VAR- for Savings Behavior Model:

Roots of companion matrix 1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

-0.75

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

325 Graph (35): Stability Test (One Step and N Down Chow Test) of - 3 Lags VAR- for Savings Behavior Model: 2010 2010 2010 2010 2005 2005 2005 2005 1% 1% 1% 1% 1% 1% 1% 1% 2000 2000 2000 2000 1upRealInterestqoq 1upCHOWs NdnRealInterestqoq NdnCHOWs 1995 1995 1995 1995 1.0 0.5 0.0 1.0 0.5 1.0 0.5 1.0 0.5 2010 2010 2010 2010 2005 2005 2005 2005 1% 1% 1% 1% 1% 1% 1% 1% 2000 2000 2000 2000 1uprgpspc 1upm2gdpratio Ndnrgpspc Ndnm2gdpratio 1995 1995 1995 1995 1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 1.0 0.5

326 Graph (36): Scaled Residuals of - 3 Lags VAR- for Savings Behavior Model:

VECM:

0.050 r Drgpspc 0.025

0.000

-0.025

1995 2000 2005 2010

1 r DRealInterestqoq

-1 1995 2000 2005 2010 0.10 r Dm2gdpratio 0.05

0.00

-0.05

1995 2000 2005 2010

327 Graph (37): Diagnostic Graphs of Parsimonious VECM: 10 10 5 5 ACF-r:Drgpspc ACF-r:Drgpspc ACF-r:Dm2gdpratio 0 0 1.0 0.5 0.0 1.0 0.5 0.0 -0.5 -0.5 5.0 N(0,1) N(0,1) 2.5 N(0,1) N(0,1) 2.5 0.0 0.0 r:Drgpspc r:Dm2gdpratio Density Density -2.5 -2.5 0.4 0.2 0.5 0.4 0.3 0.2 0.1 2010 2010 2000 2000 r:Drgpspc(scaled) (scaled) r:Dm2gdpratio 3 2 1 0 3 2 1 0 -1 -1 2010 2010 Fitted Fitted 2000 2000 Drgpspc Dm2gdpratio 0.1 0.0 -0.1 0.02 0.00 -0.02

328