Monetary Policy Transmission in Zambia: Pass-Through from the Bank of Zambia Policy Rate to Commercial Banks Market Interest Rates

Cleopatra Ngoma* and Cosam Chanda* Bank of Zambia

Abstract

The study seeks to estimate the extent of the pass-through from the policy rate to retail interest rates via the interbank rate during the period 2012.4-2019.12 when the monetary policy regime changed to interest rate targeting from the previous monetary aggregate. A two-step estimation of vector error correction model using the cointegration approach is proposed. The study suggests the existence of a complete pass-through from the policy rate to the interbank rates. However, an asymmetric and incomplete pass-through from the interbank rate to the banks’ retail rates was established. A weak relationship between the interbank rate and retail rates, especially in the short-run maybe due to the existence of imperfections in the retail market.

*Bank of Zambia, P.O. Box 30080, Lusaka, 10101. Zambia. E-mail: [email protected] and [email protected] Tel: +260 211 399 300. The views expressed in this paper do not in any way represent the official position of the Bank of Zambia. The authors remain responsible for all the errors and omissions.

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Table of Contents Abstract ...... 1 1.0 Introduction ...... 3 2.0 Monetary Policy Framework and Structure of Interest Rates in Zambia ...... 6 3.0 Literature Review ...... 11 3. 1 Theoretical Review ...... 11 3.2 Empirical Review ...... 13 4.0 Model Specification, Methodology and Data ...... 14 4.1 Model Specification ...... 14 4.2 Methodology ...... 18 5.0 Presentation and Discussion of the Findings ...... 20 6.0 Conclusion and Policy Recommendations ...... 26 7.0 Dissemination of Results ...... 28 References...... 34

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1.0 Introduction

Effective monetary policy plays a vital role in achieving macroeconomic stability. For developing countries, and in particular Zambia, a lack of well-functioning financial markets limits the effectiveness of monetary policy (Simpasa, 2014). Monetary policy entails actions by the central bank to influence either the money stock or interest rates in order to achieve the desired objective (Loayza and Schmidt, 2002). One of the most important aspects of monetary policy is the ability of central bank to influence market interest rates through its influence on short-term money market rates. This in turn influences aggregate demand and consequently inflation (Zgambo and Chileshe, 2014).

The transmission mechanisms of monetary policy is the process through which decisions by the central bank affect the economy in general and the price level in particular (Reserve Bank of South Africa, 2018). The different channels through which monetary policy can be transmitted though include the: Interest Rate Channel: This operates through changes in the key monetary policy rate, which directly affect banks' lending rates given that the Policy Rate is the reference rate for banks' pricing of their credit products.

Exchange Rate Channel: Changes in the central bank policy Rate also works through the exchange rate channel. For instance, an increase/decrease in the Policy Rate results into an exchange rate appreciation/depreciation thereby leading to a decline/increase of consumer prices.

Credit Channel (Balance sheet channel): This is an important channel in countries where banks have an important role within the financial system. In conditions when there is no full redundancy in bank deposits of economic entities other sources of funds. For instance, if the central bank undertakes an expansionary monetary policy, there is a corresponding rise in reserves and banks’ deposits which in turn results into a rise in the volume of bank loans. This in turn, results into an increase in investment and gross domestic product. The opposite is true when a contractionary monetary policy is undertaken. In other words, the credit channel is also known the balance sheet channel in the sense that interest rates affect the balance sheet, cash flow and net value of firms and consumers. More interest rates affect less cash flow, less net value, reducing the loan, fall in aggregate demand and growth in importance of negative selection and moral hazard

Expectations Channel: This works through the expectations economic agents form about key macroeconomic variables, such as the exchange rate. For instance, expectations of a depreciation in the exchange rate is likely to result in upward revision of consumer prices, thereby exerting inflationary pressure on overall prices. An increase in consumer prices will result in economic agents' expectation of higher interest rates in the future to contain inflationary pressures, a situation which may lead to increased aggregate demand and heightened inflationary pressures in the short-run (Murić,2010).

A critical and most important issue in the monetary policy transmission mechanism is the pass-through defined as the degree and speed of adjustment with which a change in the monetary policy instrument is passed on to the economy (Aydin, 2007). The magnitude and speed of adjustment of the lending and deposit rates determines whether monetary policy is effective or not. Aziakpono, Wilson and Manuel (2007) assert that if the response of the retail interest rates is too small to be noticed or delayed or sluggish, monetary policy may not achieve its desired goal irrespective of the size or magnitude of the change in the official rate.

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According to Monti (1971), under perfect competition, banks are able to keep retail rates and wholesale rates close to each other most of the time. This is because any change in wholesale rates tends to be reflected in retail rates within a relatively short period of time (Banerjee, Bystrov and Mizen, 2012). Aziakpono and Magdalene (2010) argue that if monetary policy is to be effective, changes in the monetary policy rate should be transmitted to other interest rates quickly and the magnitude of the change should be large enough to influence investment and consumption. If the opposite happens, then it becomes ineffective and interest rates tend to be sticky (Fuentes and Ahumada, 2003). Interest rates tend to be sticky as commercial banks usually have other factors that they may consider along with the central bank set benchmark rate. These factors may be bank and industry specific as well as macroeconomic in nature. For instance, Gambacorta (2004) argues that commercial banks interest rates on loans depends positively on real gross domestic product (GDP) and inflation as better economic conditions support profitable projects and thereby increase credit demand. Azeez and Gamage (2013) indicate that interest rates also depend on the degree of operational efficiency, strength of regulation and the competitiveness of the banking industry. Therefore, all these factors tend to affect the degree and speed of monetary policy pass-through to retail bank interest rates.

Empirical evidence on interest rates pass-through across the world is mixed. A few studies have reported a complete interest rates pass-through (Sander and Kleimeire, 2006; Hofman and Mizen, 2001; Kwapil and Schaler, 2010) in developed countries such as the United Kingdom (UK) and the United States of America (USA). On the other hand, in developed and developing countries, sluggish and incomplete interest rate pass-through (less than one-to-one adjustment of market interest rates in response to a change in the policy rate) is widely reported (Bondt, 2002; Tieman, 2004; Das, 2015; Aziakpono and Magadelene, 2010). Studies that have considered the nature of adjustment (symmetric/asymmetric) often lend support to asymmetric adjustment in both developed and developing countries (Hofman and Mizen, 2004; Mojon 2000; Wang and Lee, 2009; Karagamis and Vlamis, 2010; Chileshe and Oluseguni, 2016) with a few outliers such as Jamillov and Egert (2014) reporting symmetric adjustment process. Some of the reasons cited for cross country variations include the orientation of monetary policy, liberal or controlled monetary policy regime (Gidlow, 1998; and Egert et al., 2007), presence or absence of formal accountability and transparency measures in the monetary policy process (Kaketsis and Sarantis, 2006; Kleimeier and Sander, 2006; Gambacorta, 2008; and Liu et al., 2008), stage of financial market development, degree of financial market openness, concentration within the banking sector (Cottarelli and Kourelis, 1994; Borio and Fritz, 1995; Mojon, 2000; and Weth, 2002), asymmetric information (Stiglitz and Weiss, 1981), switching cost and bank size (Cottarelli, Ferri and Generale, 1995; Angeloni et al., 1995; Berlin and Mester, 1999), asset quality and default risk (Reint Gopp et.al, 2007; Saborowski and Weber;2013),maturity mismatches (Weth, 2002), and menu cost (Hannan and Berger, 1991; and Hoffman and Mizen, 2004).

In Zambia, the financial and monetary policy environment has undergone some reforms since attaining independence in 1964. Notably, the liberalization of the financial sector which commenced in the early 90s resulted in the abolishment of credit, interest and exchange controls (Simatele, 2004). Further, the conduct of monetary policy has also evolved over the years in line with the changes in financial and monetary market conditions.

With a view of enhancing accountability and transparency of the monetary policy process, the Bank of Zambia in April, 2012 took its first step towards the modernization of its monetary policy framework when it changed its operational procedures from monetary aggregates (quantities) targeting to interest rates (prices) targeting. The Bank of Zambia policy rate was introduced as a starting point and this came with a

4 | P a g e shift in the monetary policy operational target from reserve money to the overnight interbank rate which is expected to fluctuate between the-/+1 corridor of the policy rate(Zgambo, 2017).

Seven years down the line since the change of the monetary policy framework, there is a need for empirical evidence on how Zambia’s transition towards inflation targeting has fared so far.

Thus, despite the changing financial and monetary environment in Zambia, the degree and speed of monetary policy pass-through to retail bank interest rates has not been adequately investigated under the current monetary policy framework (transition to Inflation Targeting). However, two known studies that attempted to study monetary policy transmission during the current monetary policy framework are by Chileshe and Oluseguni (2016) and Zgambo (2017).

Even if these empirical studies making inferences on interest rates pass-through, the magnitude and speed of monetary policy transmission via the interest rates channel is not their primary focus. For instance, Zgambo (2017) concentrates on liquidity management best practices in an inflation targeting framework by making a country comparative analysis and drew lessons for Zambia. On the other hand, Chileshe and Oluseguni (2016) focused on confirming the existence of an asymmetric response of retail and bond yield rates to changes in monetary policy controlled interest rates. Further, although Chileshe and Oluseguni (2016) cover a longer time span (1992–2016), their analysis combines both monetary targeting and inflation targeting (transition) regimes which makes it difficult to assess the effectiveness of the interest rates based framework adopted in 2012.

This study seeks to build on Chileshe and Oluseguni (2016) to assess the efficacy of monetary policy transmission during the current price based monetary policy framework. The objective is to empirically establish the degree (magnitude) and speed of adjustment of commercial bank retail rates to changes in the monetary policy rate whilst investigating its nature i.e. symmetric or asymmetric adjustment. The study employs a two-step estimation approach: first from the Bank of Zambia policy rate to the interbank rate and secondly from the interbank rate to the commercial bank retail rates. This is different from a single step regression analysis where the pass-through from monetary policy to retail rates is directly estimated (unifying or monetary approach) undertaken by Chileshe and Oluseguni (2016). A unifying or monetary approach may not adequately highlight account for happens at every stage of the monetary policy transmission. On the other hand, a two-step regression approach has the potential of identifying specific areas of high flexibility or rigidities in the pass-through process that might need customized policy intervention to enhance the effectiveness of monetary policy (Sanusi, 2010).

Besides, these studies do not provide empirical explanations pertaining the underlining factors influencing the interest rates pass-through as per their findings. For instance, Chieshe and Olusegun (2017) finds the pass-through to be asymmetrical but does not test for the possible reasons behind such results.

However, one important aspect to highlight as far as monetary policy decisions are concerned is that the setting of the policy rate by the central bank is not a random event. It results from a critical analysis of macroeconomic developments in the economy. Thus, the policy rate is adjusted in order to influence future inflation, business cycle, and to improve financial systems stability if need be. Therefore, there are other reasons that commercial banks may consider in setting lending rates aside from the benchmark rate set by the central bank (policy rate). For instance, commercial banks add a risk premium to the central bank policy rate depending on prevailing macroeconomic conditions, bank specific risk assessment and changes in strategic prices. Therefore, the study controls for other factors such as competition, default risk and key

5 | P a g e macroeconomic variables that might influence commercial banks’ behavior in the adjustment of retail interest rates in response to monetary policy changes. This is of great significance in highlighting the possible determinants of the pass-through process.

Further, with regards to the monetary policy transmission channels, empirical literature on Zambia shows that exchange rate channel is the strongest monetary transmission channel. This is followed by the interest channel though it has been cited to be weak (Chileshe and Olusegun, 2017; Chileshe et.al, 2014; Zgambo and Chileshe, 2014; Funda, 2012; Bova, 2009; Mutoti, 2006;Simatele, 2004). However, the results on the credit channel are mixed for instance Cheleshe and Oluseguni (2017) finds it working while Lungu (2004) did not find it working and Simpasa et.al (2014) finds it stronger in large banks than small banks. Empirical evidence on the performance of the expectations channel of monetary policy seem not to exist. It is important to note that the empirical evidence uncovered here relates to the monetary targeting framework despite Chileshe and Olusegun (2017) having attempted to combined both, however such analysis may not yield much benefits since the set-up of the operational systems differ under these different frameworks.

Therefore, very little is known about the effectiveness of these channels under the current price based framework. Hence the findings of this study are indirectly giving feedback on the performance of the interest rates channel under the current framework.

The study is of great importance to the practice of monetary policy formulation and implementation as it provides policy markers with an insight concerning how long it takes for a particular policy action to have an impact onto the real sector of the economy. This in turn would facilitate timely monetary policy decision making and implementation as well as the selection of appropriate tools that can work at a given time so as to meet the key central bank objective price, macro-economic and financial systems stability.

The rest of the paper is organized as follows: Section 2 presents a brief description of the monetary policy framework in Zambia and the structure of interest rates. Section 3 reviews the literature. Section 4 outlines the empirical model estimated and the methodology employed by the study. Section 5 presents the findings of the study while Section 6 concludes and offer policy recommendation. Section 7 outlines the dissemination structure of the study results.

2.0 Monetary Policy Framework and Structure of Interest Rates in Zambia

The primary objective of the Bank of Zambia (BoZ) is to achieve and maintain price and financial system stability. The rationale behind price stability is to protect the value of income and savings and subsequently encourage investment flows to boost the economy’s productive capacity. This is necessary for employment and economic growth as well as the competitiveness of exports. To achieve the price stability objective, a monetary policy framework that defines the operational, intermediate, and ultimate targets as well as the instruments is used.

In Zambia, monetary policy has undergone changes overtime to suit the prevailing economic environment. Although the stages in the evolution of monetary policy in Zambia can be divided into several timeframes, there are two distinct eras. The first is the pre-liberalization period, which spans from 1964 to 1991, and the second is the liberalization era, which spans from 1992 to the present time (Bank of Zambia, 2014). Not until April 2012, monetary policy was based on the monetary aggregate targeting (MAT) framework. Under this framework, reserve money was the operating target while broad money was the intermediate target,

6 | P a g e with inflation as the ultimate objective. This monetary policy framework relied on the assumption that the velocity of money was constant, the relationship between reserve money and the multiplier was stable, and in turn broad money had a stable and predictable relationship with inflation. In this regard, the BoZ could control overall monetary conditions in the economy by keeping reserve money at a level consistent with the desired broad money growth. Deviations from the reserve money target determined the pace and aggressiveness of the BoZ’s liquidity management activities.

However, Simpasa et al. (2015) provided evidence of a weakening link between money supply and inflation. This was on the basis of a volatile and declining velocity of money accompanied by rising money multiplier contributing to the observed inverse relationship between broad money growth and inflation (Figure 1).

Figure 1: Money Multiplier, Money Supply, Velocity of Money and Inflation (1994-2011)

140.00 12.00 120.00 10.00 100.00 8.00 80.00 6.00 60.00 4.00 40.00 20.00 2.00

0.00 0.00

Jun-02 Jun-94 Jun-95 Jun-96 Jun-97 Jun-98 Jun-99 Jun-00 Jun-01 Jun-03 Jun-04 Jun-05 Jun-06 Jun-07 Jun-08 Jun-09 Jun-10 Jun-11

Dec-94 Dec-96 Dec-07 Dec-95 Dec-97 Dec-98 Dec-99 Dec-00 Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-08 Dec-09 Dec-10 Dec-11

Annual Inflation-LHS Annual Ms Growth Rate-LHS Money Multiplier (M3/RM)-RHS Velocity of money-RHS

Source: Authors’ Computation using data from Bank of Zambia

Therefore, hitting the operational target did not guarantee the attainment of the ultimate goal of low and stable inflation. This implied that MAT could no longer provide an adequate signal about the stance of monetary policy. Thus, it became difficult to deal with inflationary pressures and to assess central bank accountability.

Compelled by the challenges of achieving quantitative monetary targets and with a view of modernizing monetary policy, the Bank of Zambia embarked on the transition towards a price based inflation-targeting framework in April 2012. The starting point was the introduction of the Bank of Zambia policy rate with the ultimate objective of adopting a fully-fledged inflation targeting monetary policy framework in the near future. This signaled the central bank’s commitment to a more transparent, credible and effective monetary policy (Bank of Zambia, 2012).

The policy rate is a key interest rate that signals the stance of monetary policy. This is done in line with expected inflation over the medium-term with the interbank rate being the operating target, replacing

7 | P a g e reserve money under MAT (Zgambo, 2017). Further, the policy rate provides a credible and stable anchor to financial market participants in setting their interest rates. More precisely, commercial banks use the policy rate as the base rate when setting the price or interest rates for their loans and advances (BoZ, 2015). The policy rate also guides open market operations and is expected to influence the overnight interbank rate. The interbank rate, being a price at which banks lend to each other, is expected to influence retail market rates set by commercial banks and in turn demand for credit, aggregate demand and ultimately inflation.

With the introduction of the Bank of Zambia policy rate in 2012, a mid-rate interest rate corridor system was adopted for the policy rate. The overnight interbank rate is expected to fluctuate within the corridor, but as close as possible to the policy rate. The Bank of Zambia desires to keep the overnight interbank rate within +/- 1 percentage point of the policy rate having revised it from the initial +/- 2 percentage points until May 20171. As a result, if the overnight interbank rate tends towards the lower bound of the corridor, the BoZ undertakes contractionary open market operations to push the interbank rate up but within the corridor. Similarly, if the interbank rate tends towards the upper bound of the corridor, expansionary open market operations are undertaken to stop the overnight rate from breaching the upper bound of the corridor (Zgambo, 2017).

Figure 2 reveals some significant deviation of the interbank rate from the policy rate in 2014, 2015 and 2016. This is reflected in the interbank rate rising above the upper bound corridor of the policy rate. This was a response to the contractionary monetary policy undertaken by the Bank of Zambia to address the inflationary and exchange rate volatility shocks that hit the economy at the time. The Bank of Zambia did not carry out monetary operations to steer the interbank rate back into the policy rate corridor as it needed to allow the earlier undertaken contractionary measures of a rise in the BoZ policy rate (from 12.5% to 15.5%) and required reserve ratio (from 14% to 18%) to take effect. It was judged that expansionary open market operations would have injected liquidity in the market that could have caused further depreciation of the domestic currency (Kwacha) and hence add to inflationary pressures (Bank of Zambia, 2014; Bank of Zambia, 2016).

1 The rationale behind narrowing the policy rate corridor from +/- 2 percentage points to +/- 1 percentage point was to improve clarity of the policy stance and effectiveness of monetary policy by moderating volatility in the interbank rate.

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Figure 2: Money Markets Interest Rates in Zambia (1995-2019)

75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0

0.0

Jun-03 Jun-06 Jun-09 Jun-95 Jun-96 Jun-97 Jun-98 Jun-99 Jun-00 Jun-01 Jun-02 Jun-04 Jun-05 Jun-07 Jun-08 Jun-10 Jun-11 Jun-12 Jun-13 Jun-14 Jun-15 Jun-16 Jun-17 Jun-18 Jun-19

Dec-04 Dec-07 Dec-95 Dec-96 Dec-97 Dec-98 Dec-99 Dec-00 Dec-01 Dec-02 Dec-03 Dec-05 Dec-06 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13 Dec-14 Dec-15 Dec-16 Dec-17 Dec-18 Dec-19 Average lending rate 6-months depost rates BoZ policy rate Interbank Rate

Source: Authors’ Computation using data from Bank of Zambia

Further, figure 2 also reveals that the commercial banks’ interest rates spreads have been persistently high historically. In addition, the interbank rate was very volatile during the period 1995-2006. High volatility in the interbank rate is expected under MAT since the objective of this framework is money supply stability in order to achieve price stability. This was achieved by focusing on the deviations of money growth from a pre-announced target rather than pursuing interest rate stability. Thus, under MAT, the amount of liquidity to be withdrawn or injected in the market for purposes of meeting the reserve money target at interest rates determined by the market was of great significance. However, the interbank rate became relatively stable after 2006 with distinct stability observed after 2012, partly attributable to the adoption of price based framework in 2012 that requires the interbank rate to fluctuate within the lower and upper bound of the policy rate Post 2012, the interbank rate moves closely with the policy rate. This suggests the existence of a strong link between the policy rate and the interbank rate. However, around 2014 and 2016, the interbank rate breached the policy rate upper bound owing to the exchange rate depreciation and high inflationary pressures experienced at the time (Figure 3). The central bank could not steer it back as it had to wait for the earlier contractionary measures undertaken to take effect. Further, the data show that the interbank rate and the deposit rates closely move in tandem with one another. This proposes a strong relationship between the two interest rates implying that the policy rate adjustments are reflected in the interbank and deposit rates. On the contrary, lending rates persistently appear to deviate from the interbank rate and have remained elevated way above the interbank rate even in episodes of expansionary monetary policy suggesting. This preliminary observation suggests a weak link between the interbank rate and the commercial banks retail rates. This is comparable to the experience of other African countries such as South Africa (Aziakpono and Magdalene, 2010; Matemilola, 2015), Kenya (Berg et.al, 2018), Uganda (Okkelo, 2014; Berg et.al, 2018) and Ghana (Sakyi, Mensah and Obeng, 2016; Kovanen, 2011) implementing an inflation targeting framework. This study empirically tested this relationship to establish the extent of the correlation and responsiveness of retail retails to the policy rate via the interbank rate.

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Figure 3: Policy Rate and Interbank Rate (2012-2019) 30

25

20

15

10

5

0

Jun-12 Jun-13 Jun-14 Jun-15 Jun-16 Jun-17 Jun-18 Jun-19

Sep-12 Sep-13 Sep-14 Sep-15 Sep-16 Sep-17 Sep-18 Sep-19

Dec-13 Dec-16 Dec-19 Dec-12 Dec-14 Dec-15 Dec-17 Dec-18

Mar-12 Mar-13 Mar-14 Mar-15 Mar-16 Mar-17 Mar-18 Mar-19 BoZ policy rate Interbank Rate

Source: Authors’ Computation using data from Bank of Zambia

Figure 4 shows the relationship between the Bank of Zambia Policy rate and government securities yield rates. The rates appear to be synchronized for most of the period though some misalignment is observed especially between the policy rate and the longer term yield rates (365 Treasury bill yield rate and the 3 year bond yield rate) in some instances and this could be explained by the period to maturity. Further, post 2018, the spread between BoZ policy rate and the long-term risk-free yield rates (365 treasury and 3 year bond yield rates) has been wide.

Figure 4: Bank of Zambia Policy Rate and Government Securities Yield Rates 35

30

25

20

15

10

5

0

Jul-18 Jul-12 Jul-13 Jul-14 Jul-15 Jul-16 Jul-17 Jul-19

Nov-13 Nov-12 Nov-14 Nov-15 Nov-16 Nov-17 Nov-18 Nov-19

Mar-16 Mar-12 Mar-13 Mar-14 Mar-15 Mar-17 Mar-18 Mar-19 BoZ policy rate 91-Days T-bill Yield Rate 365 Days T-bill Yield Rate 3 Years Bond Yield Rate

Source: Authors’ Computation using data from Bank of Zambia

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3.0 Literature Review

3. 1 Theoretical Review

The interest rate pass-through and the subsequent symmetric/asymmetric adjustment process are explained by the marginal cost pricing model. The marginal cost pricing model assumes perfect and complete market conditions. It stipulates that when information in the banking system is symmetric and the market is perfectly competitive, the marginal price must equal the marginal cost. Relating this to the interest rate pass-through process, the theory implies a positive relationship between money market rates (interbank rates) rates and retail interest rates. The pricing of banks’ retail products includes a premium for maturity and risk transformation involved in their activities. Therefore, the money market rates (interbank rates) reflect marginal or opportunity costs of funds as banks rely on them for short-term borrowing. It also represents the opportunity cost of deposits for households and enterprises given the alternative possibility of investing in money markets or short-term government securities (Rousseas, 1985; Gigineishvili, 2011).

The major theories that underpin the marginal cost pricing include Mont-Klein model of imperfect competition, collusive behavior of banks, consumer behavior hypothesis, customer reaction hypothesis, switching costs, menu costs, and information asymmetry hypothesis.

The Monti-Klein model assumes that the monopolistic behavior of banks determines the interest rate pass- through from the monetary authority key interest rate to commercial banks’ lending rates. The model identifies restriction to entry into the banking sector by regulatory agencies as one of the preconditions for monopoly power which promotes bank concentration (Niggle, 1987). In highly concentrated banking markets, oligopolistic behaviour of banks may cause interest rates to be sticky and adjust asymmetrically to an increase or decrease in the official monetary policy rate (Aziakpono and Magadalene, 2013). Thus, retail bank interest rates in less competitive or oligopolistic segments of the retail bank market adjust partially and only with a delay, while bank interest rates set in a fully competitive environment respond quickly and completely (Laudadio, 1987).

Closely related to the Monti-Klein model is the collusive behavior of banks hypothesis. It relates to the degree of competition among banks and the level of concentration of the retail market. According to the collusive behavior hypothesis, banks are unlikely to reduce lending rates because they do not want to disrupt their collusive arrangement. Thus lending rates will be rigid downward with a decrease in the central bank official rate while deposit rates will move rigidly upward when the official rate is increased (Bondt, 2005; and Aziakpono and Magdalene, 2013).

The consumer behavior hypothesis stipulates that the degree of consumer sophistication about the financial markets as well as the search and switching costs associated with alternative sources of financing have a bearing on interest rate pass-through. A high proportion of unsophisticated consumers relative to sophisticated consumers along with the search and switching costs enable banks to have greater market power to adjust interest rates to their advantage. Like the collusive behavior hypothesis, the consumer behavior hypothesis suggests that lending rates are rigid downward and flexible upwards (Matemilola et al., 2015).

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Further, the customer reaction hypothesis relates to the reaction of borrowers to changes in the central bank official rate (policy rate). It states that commercial banks operating in a highly competitive environment may not increase the lending rate for fear of negative reaction from customers. Commercial banks’ deposit rates will move rigidly downward when the official rate is reduced while lending rates will move rigidly upward when the official rate rises so as to retain customers (Aziakpono and Magdalene, 2013).

According to the switching costs hypothesis, customers are unlikely to change financial products and/or institutions in search of better funding or investment terms when there are high switching costs (Heffernan and Kalotychou, 2010). Customers need to spend time and effort to find out which bank offers the best deal and may find it very inconveniencing and/or too costly to switch banks. The rigidity in interest rates may thus be attributed to the banks’ exploitation of consumers’ inertia in switching financial products and/or institutions. If banks can selectively price their products to exploit customers’ inertia, then interest rates are expected to be rigid upwards for customer deposits and downwards for loans. In other words, banks may adjust their deposit rate upwards more slowly and adjust their loan rate downwards more slowly. This will lead to asymmetry in the adjustment speed in interest rates (Liu, Dimitri and Alireza, 2008).

In the case of menu costs, banks are reluctant to change their interest rates if the changes in the benchmark interest rates are very small and/or temporary (Dutta et al., 1999). Since there are adjustment costs involved in changing retail interest rates, banks may respond slowly to temporary changes in monetary policy rate, but quickly to more permanent changes in policy rates.

According to Stiglitz and Weiss (1981), a plausible explanation for interest rate rigidity is due to asymmetric information. Information asymmetry creates an adverse selection problem in the credit markets where high interest rates attract riskier borrowers or brings about moral hazard. When banks perceive the risk of default to be high, they tend to maintain a large spread between lending and deposit rates (Aziakpono and Magdalene, 2013).

Besides the theories discussed above, another factor that could influence interest rates pass-through is the bank ownership structure i.e. state owned or private sector owned. A banking system which is dominated by state owned banks results in banking concentration or some form of monopoly. This, coupled with simple inefficiency or political pressures, may cause rigidity in interest rates adjustment as noted under the Monti-Klein models (Cottarelli and Kourelis, 1994). Further, the level of financial system development has an impact on the degree of interest rate adjustment. A well-developed financial system provides a wide range of financial instruments and intermediaries for savers and investors and therefore provides alternative sources of financing. Some alternative sources of financing include active and broad markets for treasury bills, long-term bonds (both government and private), and an active stock market. In such a developed financial system, interest rates are more flexible in response to central bank induced money markets changes because no single financial intermediary enjoys absolute market power (Aziakpono and Magdalene, 2013).

The preceding discussion has presented some theoretical groundwork that shows the several factors that could affect the interest rates pass-through process. However, it is important to note that these factors could vary from country to country and within a country as the financial environment changes. In the next sub- section, some empirical studies on this subject matter are presented.

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3.2 Empirical Review

Empirical literature suggests that interest rate pass-through differs across countries, financial institutions and financial products (Cottarelli and Kourelis 1994; Borio and Fritz 1995; Hofmann and Mizen, 2004; Mbotwe, 2015; Chileshe and Olusegun, 2017). This study therefore highlights the literature on the speed and nature (symmetrical/asymmetrical) of monetary policy pass-through for developed economies as well as emerging market and developing economies.

In developed economies with well-functioning and developed financial markets such as the USA and UK, there is no consensus among scholars on the nature and adjustment dynamics of the pass-through. Thus, while some studies such as Altunbas, Fazylov, and Molyneux (2002), Bernanke and Gertler (1995), Cook (2008) and Kashyap and Stein (2000) report complete and fast pass-through, others such as Bondt (2002), Hofmann and Mizen (2004), Liu et al. (2008) and Mojon (2000) conclude that the pass-through is incomplete and asymmetric due to the presence of banks’ collusive behavior, adverse customer reaction as well as heterogeneity in competition across countries in financial markets and retail bank products (Karagiannis and Vlamis, 2010). For instance, while the banking system plays a more significant role in lending in Europe, its role is limited in the USA.

Mixed results for emerging market and developing economies outside Africa are also present. While Haughton and Iglesias (2011) find evidence for a complete and symmetric pass-through for Organization of Eastern Caribbean States (OECS), Wang and Lee (2009) find contrasting results for the other countries of the Caribbean Single Market countries. The variation in results was attributed to the existence of information symmetry within the financial markets of the OECS contrary to the Caribbean Single Market countries. Further, Jamilov and Egert (2014) found mixed results on the symmetrical/asymmetrical nature of the pass-through of five Caucasian economies (Armenia, Azerbaijan, Georgia, Kazakhstan and Russia). Applying the ARDL approach, the study found evidence of symmetrical adjustment for Armenia, Azerbaijan, and Russia on one hand and asymmetrical adjustment for Georgia and Kazakhstan on the other hand due to market structure differences in these economies. Mishra and Montiel (2012) investigated the effectiveness of monetary transmission in developing countries by employing a vector auto regressive (VAR) model. The results suggested that monetary transmission appears to be weak and incomplete in developing countries alluding their findings to the underdevelopment of the financial markets in the developing world . Das (2015) provides evidence on the credit channel of monetary policy transmission in India by using a two-step estimation of vector error correction model. The results indicate the existence of incomplete, slow and asymmetric pass-through from the changes in the policy rate to bank interest rates in India though there have been some improvements following the introduction of new the new base rate system in 2010.

Aziakpono, Magdalene and Manuel (2007) employed an asymmetric error correction model proposed by Scholnick (1996) to examine how market interest rates in South Africa adjust to changes in the South African Reserve Bank (SARB) official rate under different monetary policy regimes. Their findings indicate that the pass-through is incomplete and asymmetric as the speed of adjustments was higher during contractionary than expansionary periods. The study attributes asymmetric behavior of retail interest rates adjustment to the negative customer reaction and collusive pricing behavior of banks. Mbowe (2015) also assessed the degree and speed of adjustment of commercial banks’ interest rates to monetary policy rate changes in Tanzania by employing an error correction model. The empirical findings lend support to incomplete monetary policy rate pass-through to commercial bank short-term interest rates both in the short

13 | P a g e and long-run due to the underdevelopment of the financial sector in Tanzania. By splitting the sample into two periods, the results do not support the view that the policy rate pass-through in Tanzania had improved over time. However, this study did not take into account the symmetric/asymmetric interest rate adjustment dynamics.

In Zambia, most empirical evidence has mostly focused on general monetary policy transmission process (Simatele, 2004; Mutoti, 2006; Chileshe et al., 2014; Zgambo and Chileshe, 2014), the determinants of interest rates pass-through (Banda, 2010), and the bank lending channel of monetary policy pass-through (Simpasa, Nandwa and Nabassaga, 2014). Emphasis on the magnitude and speed of adjustment has not been explored, and yet very critical for the assessment of the effectiveness of monetary policy. However, Chileshe and Olusegun (2016) analyzing the symmetric/asymmetric nature of interest rates pass-through in Zambia by employing a non-linear ARDL approach. The study results lend support for the existence of low and asymmetric adjustment of retail and bond yield rates to changes in policy-controlled interest rates (interbank and 3-month Treasury bill rate). Zgambo and Chileshe (2014) estimated the interest rates pass- through from the interbank rate to commercial bank lending interest rates for the period 1995-2014. By employing an error correction model, the study found that the interest rate pass-through was slow and low in both the short and long-run. However, the pass-through improved somewhat after 2001.

Asset quality and default risk tend to have a bearing on the degree of monetary policy pass-through. According to Reint et.al (2007), banks with weak balance sheets may react to an expansionary monetary policy stance by shoring up liquidity rather than extending credit at lower interest rates. A change in the policy rate may thus have a limited impact on market rates. In essence, potential new loans are crowded out by the presence of bad loans on the balance sheets. Saborowski and Weber (2013) in their assessment of the determinants of interest rate pass-through in developed and emerging market economies measured asset quality as the banking sector‘s non-performing loan (NPL) share in total credit. They found that countries with low NPLs had a long-term pass-through of about 11 percent higher than that of countries with high NPLs.

Further, the degree and magnitude of interest rates pass-through could be affected by bank competition. Thus, banks’ adjustment of interest rates could be on the basis of what other banks are doing. According to Simpasa (2013), Mutoti (2011) and Musonda (2008), the Zambian banking industry is highly concentrated and monopolistic in nature with the largest four private banks accounting for over 74% of total banking assets and in excess of 67% of total banking sector deposits. This is consistent with empirical findings by Greenwood-Nimmo (2010) that retail rates might be rigid downwards due to market structure in the banking system especially if the market is oligopolistic. In a monopolistic banking industry, banks are reluctant to make adjustments to their retail rates.

4.0 Model Specification, Methodology and Data

4.1 Model Specification

Theoretically, the relationship between money market and retail rates is explained through the marginal cost pricing model proposed by Rousseas, (1985) and modified by Bondt (2002). The model assumes perfect competition with complete information and equality of prices with marginal costs (Tai, Sek, and Har, 2012; Rousseas, 1985). The application of this to the price-setting behavior of banks results in the following marginal cost pricing equation (Bondt, 2002)

14 | P a g e

br = 훾0 + 훾1mr − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − (1) where 푏푟 is the price set by banks (bank interest rate), 푚푟 is the marginal cost price approximated by a comparable market interest rate, γ0 is a constant markup, and γ1is a measure of interest rate pass-through: if γ1 is equal to 1, there is complete pass-through or unitary interest rate elasticity of demand for deposits and loans (Coricelli, Egert, and McDonald, 2006), implying markets are perfect (full information and perfectly competitive) and banks are risk-neutral; and when γ1 is less than 1, there is incomplete pass- through such that banks have some degree of market power.

The study proposes a two-step approach in determining the extent of monetary policy pass-through to market interest rates as outlined in equations 2 and 3 by making modifications to equation 1.

Step 1: Pass-Through to Interbank Rate from the Policy Rate

In step 1, the pass-through is determined from the policy rate to the interbank rate (operating target) as follows:

퐼푁푇푡 = 훽0 + 훽1푃푅푡 + 휀푡 − − − − − − − − − − − − − − − − − − − − − − − − − − − −(2)

퐾

Δ퐼푁푇푡 = 훿0퐸퐶푇푡 + ∑ 훿1 ∆ 퐼푁푇푡−퐾 + 훿2∆푃푅푡−퐾 + 휀푡 − − − − − − − − − − − − − −(3) 퐾=1

where 퐼푁푇푡 is the interbank rate; 푃푅푡 is the Bank of Zambia policy rate; 훽1 is the coefficient measuring the pass-through from the policy rate to the interbank rate; if 훽1 is equal to one, then there exist complete pass-through, when it is more than one then there is overpass though or overshooting and when it takes the value of less than one then pass-through is incomplete or there is interest rate stickiness (Aziakpono and ̂ ̂ Wilson, 2010; De Bondt, 2005); 퐸퐶푇푡 = 퐼푁푇푡−1 − 훽푂 – 훽 1 푃푅푡−1 is the error correction term measuring period t-1 deviation from the long-run stationary relationship through coefficient 훿0; and 휀푡 is the error term. Equation 2 measures the long-run relationship and equation 3 is the short-run relationship for the two variables under study.

Step 2: Pass-Through to Commercial Bank Retail Rates from the Interbank Rate

In step 2, the study seeks to measure the extent of pass-through from interbank to bank retail rates (lending and deposit rates) specified as follows:

퐶푅푅푡 = 휃0 + 휃1퐼푁푇푡 + 휀푡 − − − − − − − − − − − − − − − − − − − − − − − − − − − (4)

퐾

Δ퐶푅푅푡 = 훼0퐸퐶푇푡 + ∑ 훼1 ∆ 퐶푅푅푡−퐾 + 훼2∆ 퐼푁푇푡−퐾 + 훼3∆ 푋푡−퐾 + 휀푡 − − − − − − − −(5) 퐾=1 where 퐶푅푅푡 is a measure of commercial bank retail rates in this study represented by the 6-month deposit and average lending rates; 퐼푁푇푡 is the interbank rate; 휃1 is the coefficient measuring the degree of pass-

15 | P a g e through from the interbank rate to retail rates; 푋푡 is a vector of risk-adjusted variables that banks consider in adjusting their retail rates that has impact on their balance sheet and therefore profitability; 퐸퐶푇푡 = 퐶푅푅푡−1 − 휃0 − 휃1INT푡−1 − 휃2X푡−1 is the error correction term measuring period t-1 deviation from the long-run stationary relationship through coefficient 훿0; and 휀푡 is the error term. As reported in step 1, equations 5 and 6 are respectively long-run and short-run relationships.

For the short run dynamics in step 2, to assess which variables in the X vector affect the determination of retail rates by commercial banks aside the interbank rate, the study estimates single equation ECMs as described in equation 5. The models are estimated with OLS for the period April 2012 to December 2019 including upto 4 lags. We use general-to-specific modeling, starting with general models that include error correction terms of the lending rates and deposit rates and changes in variables as outlined in equation 5. The reduction of the general model, to get a parsimonious model is executed using Autometrics, a computer- automated general to- specific modeling approach in the Oxmetric software under pCgive modelling. According to Durevall etal (2013), the autometrics reduction process consists of multiple-path reduction searches to avoid path dependence. If all reductions, and diagnostic and structural break tests, are acceptable then the model becomes a terminal model. If there are several terminal models they are merged into a new general model and the multi-path search iteration is repeated. When this produces more than one terminal model, the Schwarz criterion is used to pick the preferred model, but all the terminal models are reported. The authors further argue that Autometrics makes the reductions to specific models transparent, thereby facilitating replication, and increasing credibility by reducing the flexibility inherent in general-to-specific modeling

The variables included in the X vector typically reflects the indicators related to the exposure of the banking sector in Zambia. A quick look at the sectorial credit distribution in figure 3 reveals that most of the credit is extended to the households, wholesale and retail, agriculture, mining manufacturing, electricity and transport sectors. Thus, shocks to these such as a fall in copper prices as the Zambian economy is copper dependent for export earnings, significant depreciation in the Kwacha due to high dependency on imports for both final consumer and intermediate goods may affect commercial banks’ lending behavior and subsequently affect the monetary policy pass-through. This study considered the potential effects of the shock to the commodity prices (copper prices), and the exchange rate as control variables. In addition, a measure of the banking sector competitiveness and credit risks were considered as these are widely cited as a key consideration by banks in setting their interest rates. However, the maize prices were not considered as key in the determination of retail rates since they are heavily influenced by government subsidies. Government uses subsidies to promote production and manage the price of maize as it is predominantly produced by smallholders across the country.

16 | P a g e

Figure 5: Sectoral Credit Distribution (2008-2019) 12.00 30.00

25.00 9.00 20.00

6.00 15.00 K'million 10.00 K'Millions 3.00 5.00

- -

Dec 2016 Dec 2017 Dec Dec 2008 Dec 2009 Dec 2010 Dec 2011 Dec 2012 Dec 2013 Dec 2014 Dec 2015 Dec 2018 Dec 2019 Dec Agriculture, forestry,Fishing and hunting Mining and quarying Manufacturing Electricity, gas, water and energy Wholesale and retail trade Transport, storage and communications Households Total Credit (RHS) Source: Authors’ Computation using data from Bank of Zambia

The number of months that are required to achieve 50% of the pas-through are obtained by calculating the half-life. The half-life informs the degree of rigidity in retail interest rates. The higher the half-life, the higher the interest rate rigidity (slow adjustment). Conversely, a low half-life indicates low rigidity of retail interest rates (fast adjustment). Thus, in line with Das (2015), the half-life will be computed in absolute terms as follows:

log 2 ∗ 푡푖푚푒 퐻푎푙푓 퐿푖푓푒 = − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − (9) 퐼푄 푙표푔 [ ] 퐹푄 where : . 퐼푄 = 퐼푛푖푡푖푎푙 푄푢푎푛푡푖푡푦 (100) . 퐹푄 = 푄푢푎푛푡푖푡푦 푡ℎ푎푡 푟푒푚푎푖푛푠 푎푓푡푒푟 푎푑푗푢푠푡푚푒푛푡

To determine the nature of adjustment of the pass-through process when the policy rate is increased and decreased, the study will follow Aziakpono and Magdalene (2010) and split the residuals from cointegration equations 2 and 5 into two series: 퐸퐶+and 퐸퐶− as follows:

퐸퐶푇+ = 퐸퐶 푖푓 퐸퐶 > µ − − − − − − − − − − − − − − − − − − − − − − − − − −(10)

17 | P a g e

퐸퐶 = 0 푖푓 퐸퐶 < µ − − − − − − − − − − − − − − − − − − − − − − − (11)

and

퐸퐶푇− = 퐸퐶 푖푓 퐸퐶 < µ − − − − − − − − − − − − − − − − − − − − − − − − − −(12)

퐸퐶 = 0 푖푓 퐸퐶 > µ − − − − − − − − − − − − − − − − − − − − − − − − − − − − − (13)

Where µ is the mean of the residual from the cointergration equation (EC). The asymmetric specifications in equations 10 and 12 are introduced as separate variables in the form of dummy variables in the error correction model to obtain an asymmetric short-run dynamic equation specified as: 퐾 퐾 + + − − ∆퐶푅푅푡 = 휃 + 훼퐶푅푅푡−1 + ∑ 휑푗 ∆퐶푅푅푡−퐾 + ∑(훾푖 ∆퐸퐶푡−푖 + 훾푖 ∆퐸퐶푡−푖) + 휀푡 − − − −(14) 퐾=1 푖=0

+ − where 훾푖 and 훾푖 are coefficients of the the error correction term representing policy rate increases and declines, respectively. The coefficents are expected to bear the expected signs: positive and negative signs for policy rate incresease and decresease, respectively. The Wald test will be carried out to test the equality between the coefficients of the positive and negative residuals in the asymmetric error correction model to establish asymetric/asymetric adjustment.

4.2 Methodology

Similar to Mbotwe (2015) and Das (2015), this study proposes to employ a cointergration approach in estimating equations 2 and 5 as well as the corresponding short-run relationships. It is important to note that when variables are integrated of the same order, the common methods to use are the Engle and Granger (1987) and Johansen and Jeselius (1994) cointegration procedures. However, unlike the Engle and Granger approach which involves an estimator obtained in two stages where possible errors introduced in the first stage are transferred to the second stage, the Johansen cointegration method is based on estimates of the matrix rank and its eigenvalues are obtained in a single stage. Further, unlike the Engle-Granger approach to cointegration that is sensitive to normalization and can result in conflicting conclusions depending on the variable chosen as the dependent variable, the Johansen test results by contrast is invariant to the choice of the variable selected for normalization and this avoids conflicting of conclusions. It is also easy to derive an error correction model (ECM) under this approach through a simple linear transformation which integrates short-run adjustments with long-run equilibrium without losing long-run information.

A general to specific model estimation procedure was employed. Thus, an initial general model that contains a wide range of variables that are relevant to the subject matter is estimated using the proposed method. An initial model estimation resulted into an elimination statistically insignifant variables leaving a parsimonious model.

4.3 Data The study used monthly data for the period April 2012 to December, 2019 being the period when the monetary policy regime changed to interest rate targeting from the previous monetary aggregate.

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4.3.1 Description of Variables This paper employs key monetary and macroeconomic variables in the interest rates pass-through process summarized in the table below:

Table 1: Summary of the Data

S/N Variables Formal Name Unit of Source Measurement 1 IB Bank of Zambia Policy Rate Percentage Bank of Zambia 2 IB Inter-bank Rate Percentage Bank of Zambia 3 LR Average Lending Rate Percentage Bank of Zambia 4 DR Deposit Rate Percentage Bank of Zambia 5 TBR Treasury Bill Rate Percentage Bank of Zambia 6 E Kwacha/US Dollar exchange rate Kwacha/US Dollar Bank of Zambia 7 LI Learner Index Ratio Computed 8 NPLS Non-Performance Loans Millions of Kwacha Bank of Zambia 9 RR Statutory Reserve Ratio Ratio Bank of Zambia 10 CUP Copper Prices Millions of Kwacha Bank of Zambia

4.3.2 Bank of Zambia Policy Rate (PR) The Bank of Zambia monetary policy rate is a key tool used to signal the Bank of Zambia’s monetary policy stance. It also provides a credible and stable anchor to financial market participants in setting their own interest rates.

4.3.3 Interbank Rate This is the rate at which banks access credit in the interbank money market on an overnight basis. The interbank money market is the conduit through which monetary policy decisions are transferred into the retail banking sector.

4.3.4 Average Lending Rate Average lending rate is the average rate cost of funds when commercial banks extend credit to institutional and individual investors. This rate is mainly determined on basis of the prevailing monetary policy rate which is set by the Central Bank. Lending rates are used in this study as it transfer the monetary policy decisions into the real sector since it signifies the benchmark cost of credit.

4.3.5 Deposit Rate This is the amount of funds paid out in interest by a bank or financial institution on cash deposits. Banks pay deposit rates on savings and other investment accounts. It also transfers monetary policy decisions into the real sector.

4.3.6 Exchange Rate (Kwacha/Dollar Exchange Rate)

19 | P a g e

An exchange rate is the price of one currency in terms of another. This study uses the Kwacha/US dollar nominal exchange rate as most international transactions are conducted using the US dollar. An exchange rate shock has an implication on monetary policy pass-through since Zambia is a net importer.

4.3.7 Treasury Bill Rate

This is measured by the composite Treasury Bills rate which is a weighted average of the 91 day, 182 day, 273 day and the 364 Treasury Bills rate. This represents the risk free wholesale rate of funds that has a direct relationship with the commercial banks retail rates.

4.3.8 Lerner Index This is a measure of market power in the industry and it has been included in the study as a proxy for competition as completion within the banking sector has an implication for monetary policy pass-through.

4. 3.9 Non-Performing Loans This is the value of loans that are in default or near default and this captures the impact of market and credit risk in the banking sector which has an influence interest rates pass-through. These are loans that have not been serviced for more than 90 day period.

4.4.0 Copper Prices (CPP). Prices of copper per metric ton to proxy for commodity prices. The study has gone for copper prices instead of maize prices because copper is the main export, thus a shock on the copper price has a multi-sectoral impact on the economy. I addition, a shock on the copper prices affect government revenues which in turn may affect the credit worthiness of households whose source of income is inclined to government activity.

4.4.1 Statutory Required Reserve Ratio. It is the percentage of deposits which commercial banks are required to keep as cash according to the directions of the central bank. The statutory required reserve ratio was also included as it is an actively used policy tool in Zambia along-side the policy rate.

5.0 Presentation and Discussion of the Findings

5.1 Stationarity Tests

To ensure that statistical problems normally associated with time-series data are avoided, stationarity tests are performed on the data by employing both the Augmented Dickey- Fuller (ADF) and Phillips-Peron (PP) unit root tests for stationarity both at level and first differences. The study employs two types of tests for robustness checks. The ADF and PP unit root results show that the variables are all integrated of order one, implying that they become stationary after the first difference. Table 2 gives a summary of the results.

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Table 2: Unit Root Results

Variable NameADF level ADF 1st DifferencePP level PP 1st Difference Order of Integration

IB -1.782318 -3.776161*** -1.509547 -8.937617*** I(1)

IB -2.603969 -6.894672*** -2.25048 -6.894672*** I(1)

LR -1.620567 -5.024542*** -0.785658 -.331602*** I(1)

DR -1.696443 -6.668505*** -1.628279 -6.643017*** I(1)

E -1.798691 -10.67031*** -2.015164 -10.72347*** I(1)

LI -1.782412 -13.44770*** -2.164557 -14.12501*** I(1)

NPLS -0.010346 -7.411890*** -0.026059 -7.050173*** I(1)

CUP -0.903297 -8.938562*** -1.107779 -34.58881*** I(1)

IB -0.134035 -8.046840*** -0.684932 -8.122040*** I(1)

IB -1.728584 -5.902072*** -1.406574 -5.988017*** I(1)

RR -1.161607 -3.607211*** -1.557923 -6.244610*** I(1)

*** indicates significance at 1 percent

5.3 Lag Length Selection Criteria

The lag length selection criterion was used to determine the optimal number of lags to be applied in the autoregressive (AR) model. Based on the Akaike Information Criterion (AIC), it was revealed that one lag was appropriate for the model estimating the pass-through from the Bank of Zambia policy rate to the interbank rates and from the interbank rate to the deposit rate. Using the same criterion, four lags were appropriate for the model estimating the pass-through from the interbank rate to the average lending rates (Appendices A1 to A2).

Notable to mention is that according to the principal of parsimony in selecting lag lengths, if two or more models explain the same phenomena but have different lag lengths, choose the model with lower lags to avoid losing information when higher lags are included and to preserve the degrees of freedom (Zgambo and Chileshe, 2014). It is for this reason that the selected lag lengths have been settled for.

5.4 Johansen Co-Integration Approach

After determining that all the variables were integrated of order one, the study undertook a Johansen co- integration test. Under this approach, the decision rule is that reject the null hypothesis of no cointegration if the value of the trace and max-eigen statistics are greater than 5% critical value, otherwise fail to reject the null hypothesis and conclude that there is no cointegration. The results reveal the existence of a co- integrating relationship among the variables of interest (Appendix B)

According to Geda and Tafere (2011), if there is evidence of co-integration in the data, it is appropriate to estimate an error correction model. Thus, the existing long run relationships were analysed using the

21 | P a g e equilibrium correction model and regression results are presented below starting with the long-run relationship and thereafter look at the short-run analysis (Aziakpono and Magda, 2010).

5.4 Long-Run Pass-through Analysis

The Johansen long-run results are provided in the table below.

Table 4: Long- Run Results

Policy Rate to Interbank Rate to Average Interbank Rate to Interbank Rate Lending Rates Deposit Rates LONG-RUN 1.639349** 0.636977** 0.495825**

(0.19382) (0.014101) (0.07352)

Standard errors in parenthesis

*Indicates significance at 5%

In the long-run, the policy rate has a positive impact on the interbank rate, thus, a unit change in the policy rate causes a 1.6 change in the interbank rate. The wald test was conducted to check if the coefficient (1.64) was statistically different from one. The results show that there is no statistical difference2 thus indicating that there is a complete pass-through of the policy rate to the interbank rate. This finding is in line with Chileshe (2016) finding that the transmission of monetary policy to the interbank money market rate, the first stage of monetary policy transmission, is strong.

Further, there is a statistically significant positive but incomplete relationship between interbank rate and commercial banks market interest rates. That is, a one percentage increase in the interbank rate results into an increase of 0.636977 percentage points and 0.49 percentage points of lending and deposit rates, respectively in the long-run. This implies that whenever the interbank rate changes, about 64 percent of the change is passed on to the lending rate and 50 percent passed on to the deposit rates. The compete and strong pass-through the policy rate to the interbank rate could be attributed to the policy rate corridor that limits the fluctuations of the latter.

These findings are consistent with previous studies in emerging and developing economies that found greater monetary policy pass-through to interest rates with shorter maturities. For instance, Liu et.al (2005) in New Zealand, Das (2015) in India, Aziakpono and Magdalene (2014) in South Africa, Mbowe (2015) in Tanzania, Zgambo and Chileshe (2014) in Zambia found incomplete but high interest rates pass-through from the monetary policy rates to money interbank rate. On the best case scenario, Cottarelli and Kourelis (1994), Borio and Fritz (1996), Kleimeier and Sander (2000), Donnay and Degryse (2001), Tobia et.al (2012), Chileshe and Olusegun (2017) and Yildrim (2012) found a complete long-term pass-through in the first stage of the monetary policy transmission. Such findings imply that monetary policy pass-through

2 F-statistic= 2.149642 df=(1, 80) prob= 0.1465

22 | P a g e exhibits some decreasing effect since the transmission works through the different stages of the transmission mechanism before it eventually reaches the ultimate goal of the policy.

5.5 Short-Run Pass-through Analysis

Table 5 shows the short-run results that include an error correction term extracted from the long-run equations. See appendix C for parsimonious actual outputs.

Table 5: Summary of Short- Run Results

Policy Rate to Interbank Rate to Interbank Rate to Interbank Rate Average Lending Rates Deposit Rates SHORT- RUN -0.394252 -0.149501 -0.188359 (0.089351) (0.014101) (0.04843) Standard errors in parenthesis

In the short run, for the pass-through from policy rate to interbank rate, the first and second lags of monetary policy Rate were significant, inducing 0.88 and 1.16 changes in the interbank rate for their respective unit increase. The first three lags of the policy rate showed a positive relationship with the interbank rate as expected while the fourth bore a negative sign. Nonetheless, using the wald test, the coefficients were found to be jointly significant3. This result suggest that there is persistence of the policy rate influence to the interbank. With regards to the pass-through from the interbank rate to the deposit rate, the deposit rate is impacted by its self by a lag of one, by the interbank rate with a lag of four and by the risk free treasury bills composite rate at a lag of one and four respectively. Coming to the lending rates, it has been revealed that the lending rate was positively but weakly impacted interbank rate with a lag of one. However, the statutory reserve ratio with a lag of one had relatively strongly strong impact on the lending rates. This is on the basis that required statutory reserve ratio is one of the key policy tools used to manage liquidity in the interbank money market by the central bank of Zambia. Changes in the statutory reserve ratio, have a direct impact on the amount of loanable funds. This observation is similar to the Indian case where Vegh (2012) argues that an actively used policy tool though not in many countries.

Looking at the adjustment parameters, the study finds an error correction term (ECT) between the policy rate and the interbank rate to be -0.39 which is statistically significant and correctly signed. This indicates that, when there is a deviation from equilibrium between the interbank rate and policy rate, the interbank rate adjusts by 40% per time period (1 month) towards the policy rate to re-establish equilibrium. At this rate, it would take about 4 months to achieve 50% of the pass-through from an increase in the policy rate. This supports the findings by Mbowe (2015) that in the error correction model, the short-term and adjustment coefficients of the policy rate pass-through to the interbank rate were statistically significant.

3 F-statistic= 3.512886 df= (4, 78) prob= 0.0109

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The ECT between the interbank rate and the deposit rate is -0.19 and resulting into a speed of adjustment of 19% towards the long-run equilibrium when a shock occurs. At this rate, it would take about 10 months to achieve 50% of the pass-through.

Further, the ECT between the interbank rate and lending rate is -0.15 and is statistically significant. This indicates that the speed of adjustment towards the long-run equilibrium given that a shock occurs is 15% and at this rate it would take about 12 months to achieve 50% of the pass-through. Das (2016) found similar results in India where the speed of adjustment between the key monetary policy rate and lending rate was 4.2 % per two weeks period resulting into 8.1 months to achieve 50% of the pass-through.

The short-run differences in the size of the pass-through from interbank rate to the lending and deposit rates respectively would be as a result of maturity mismatches between the two banks interest rates under consideration. For instance, the tenure for the deposit rate in analysis is 180 days (6 months) while the lending rate is the average for all loans advances that include long-term loans with the average tenure for long-term loans being three years.

Another possible explanation would be that under the current operational framework, commercial banks are not at liberty to set their base rate since the policy rate is their base rate as compared to the previous regime (MAT) when they were at liberty to set their own base rate. Adjusting their lending rate each time the policy rate change would negatively affect their profit margins. Instead, commercial banks find it easier to make their adjustments through the deposit rates in their pursuit of maintaining high interest spreads for profit maximization. The adjustment of deposit rates especially during the expansionary episodes implies cost reduction on their part.

In line with the consumer behavior hypothesis (Matemilola, 2015), another additional possible explanation why the speed of adjustment of 9% and 19% for the lending and deposit rates, respectively would be that lending rates face relatively stronger market resistance than deposit rates. That is, while the commercial banks may try to adjust the lending and deposit rates to protect their profit margins, the borrowers may have relatively more market power than the depositors to resist upward pressure on interest rates. Even if the commercial banks were to behave with monopoly power, they would not completely determine both the volume and price of credit as customer reaction could have an impact. Thus, the credit demand dynamics play a key role in lending rates determination.

Evidence on the weak short-run pass-through is an indication that it may take a considerable long time before the effect of a monetary policy action, such as change of official policy rate can be felt.

Table 6: Half Life Summary

Policy to Rate Interbank Rate Interbank Rate

Interbank to Lending Rate to Deposit Rate Initial Quantity 100 100 100 Final Quantity 60 85 81 HALF-LIFE IN MONTHS 4 12 10

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5.6 Impulse Response Analysis under VAR

Impulse response functions displayed in Figure 5 (north east quadrant) and figure 6 (north east quadrant) depicts the cumulative impulse response functions (IRF) for the interbank rate as a reaction to a 1% positive shock in the policy rate and for the average lending rates as a reaction to a 1% shock in the interbank rate, respectively.

Figure 5 shows that the interbank rate has a delayed response to an immediate increase in Policy Rate but within the first month, it begins to reacts strongly. With an unanticipated positive shock on the Policy rate, the interbank rate rises for about 4 months, up to roughly 1.7%, and then starts to gradually decline. Correspondingly, a positive shock on the interbank rate, depicted in figure 2, shows an initial delay in response by the average lending rate. However within the first month, this shock induces a rise in average lending rate which reaches a peak in 7 months at 0.5%, and then begins to fall.

Figure 6

Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.

Response of INTERBANK_RATE to INTERBANK_RATE Response of INTERBANK_RATE to BOZ_POLICY_RATE

2.0 2.0

1.5 1.5

1.0 1.0

0.5 0.5

0.0 0.0

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Response of BOZ_POLICY_RATE to INTERBANK_RATE Response of BOZ_POLICY_RATE to BOZ_POLICY_RATE .8 .8

.6 .6

.4 .4

.2 .2

.0 .0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

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Figure 6

Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.

Response of AVERAGE_LENDING_RATE to AVERAGE_LENDING_RATE Response of AVERAGE_LENDING_RATE to INTERBANK_RATE

.4 .4

.2 .2

.0 .0

-.2 -.2 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Response of INTERBANK_RATE to AVERAGE_LENDING_RATE Response of INTERBANK_RATE to INTERBANK_RATE

1.5 1.5

1.0 1.0

0.5 0.5

0.0 0.0

-0.5 -0.5 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

5.7 Asymmetric Speed of Adjustment

The preliminary findings seemingly suggest evidence of asymmetry in the pass-through of monetary policy to the commercial bank retail rates. Appendix D shows that the coefficients on the differences of error correction terms, ect positive and ect negative respond differently to policy rate increases and declines. The deposit rates respond faster to expansionary monetary policy episodes relative to contractionary episodes. This is related to the findings of Das (2015), whose paper showed that the deposit rates adjusted downwards to monetary policy loosening as there were statistically significant. On the lending rates, the coefficients on the error correction terms carried the expected signs but were both insignificant.

6.0 Conclusion and Policy Recommendations

6.1 Summary

The aim of this study was to provide evidence on the monetary policy transmission in Zambia by investigating the pass-through from the Bank of Zambia policy rate to the commercial banks market retail rates via the interbank market. Thus, the study examined the magnitude and speed of the Bank of Zambia Policy Rate pass-through to commercial banks’ market retail rates (lending and deposit) and the symmetric/asymmetric behavior of the pass-through in line with the kind of monetary policy stance (expansionary or contractionary) undertaken. A Johansen cointegration approach was carried out to establish the long-run relationship among the variables. Having established cointegration among the variables, the study estimated a two-step VECM from the policy rate to the interbank rate and from the

26 | P a g e interbank rate to commercial bank’s lending and deposit rates. The half-life analysis was then undertaken to establish the number of months that are required to achieve 50 percent of the pass-through. Finally, to establish the nature of the pass-through process (symmetry/asymmetry adjustment) during the rise and fall of the policy rate, two new series +and −representing contractionary and expansionary monetary policy, respectively were created from the residuals of the policy rate and interbank rate cointegration equation.

The long-run results suggest the existence of a high and complete pass-through from the Bank of Zambia policy rate to the short-term interbank rate at 100 percent. However, the pass-through from the interbank rate to commercial bank interest rates was found to be incomplete at 90 percent and 50 percent for the lending and deposit rates, respectively. The short-run pass-through results reveal incomplete pass-through at both step one and step two with the speed of adjustment being 40 percent for the interbank rate, 9 percent for lending rates and 18 percent for deposit rates to re-establish equilibrium after a shock. At this rate, it would take about 4, 16 and 10 months to achieve 50 percent of the pass-through from the policy rate to the interbank rate and from the interbank to the lending and deposit rates respectively. Further, the study found evidence of asymmetric adjustment to monetary policy with deposit rates exhibiting an upward rigidity in response to monetary policy tightening but flexible downwards in response to monetary policy loosening.

These results seem to indicate the existence of significant but incomplete and asymmetrical pass-through of monetary policy changes to commercial bank interest rates in Zambia via the interbank market. The pass- through appears to be very strong in the first stage of the transmission process (policy rate to interbank rate). This suggests that the central bank of Zambia has succeeded in regulating liquidity supply in the system through the interbank market, thereby confirming the existence of the interest rates channel of interest rates channel of monetary policy transmission though not very strong.

In general, these findings suggest that monetary policy transmission may exhibit some decreasing effects as the pass-through becomes weaker during the second stage (interbank rate to commercial banks rates), an indication that the strength of the policy rate signal may be lost along the way during the transmission process.

These findings are instrumental in the formulation and implementation of monetary policy strategies particularly the choice of monetary policy instruments and timing of monetary policy actions that promote growth and stability in the economy.

6.2 Policy Recommendations

The declining effect of monetary transmission during the second stage of the transmission process suggests that it may take a very long time before the effect of a monetary policy action can be felt by economic agents in the real sector. The time lags inherent in the transmission process might make it difficult to conduct monetary policy. In particular, these long time lags require that the central bank of Zambia must be forward-looking in its policy decisions by anticipating what might happen in future through comprehensive economic forecasts. Forecasts with small margin of errors could enable the central bank to carry out pre-emptive rather than responsive monetary policy interventions, thereby achieving the desired impact of their actions within the intended or ideal period of time.

Accordingly, the asymmetric behavior of commercial banks in response to monetary policy changes may hinder the ability of the central bank to achieve the intended purpose when it signals to the market. There

27 | P a g e might be some structural rigidities present in the retail markets (loans and deposit) that might distort the transmission process thereby rendering monetary policy less effective and efficient. The central bank should therefore explore and identify these possible distortions and devise strategies of dealing with them to improve the impact of monetary policy. Other studies have attributed the weak and asymmetric pass- through to the limited development of the financial sector (Cottarelli and Kourelis, 1994; and Mojon; 2000). In the Zambian case, this appears to be supported by the empirical findings of Chileshe and Olusegun (2016), Simpasa (2013) and Mutoti (2011).

7.0 Dissemination of Results

The results will provide insights into the effectiveness of monetary policy under the interesting rate framework. These findings are pertinent to the monetary authorities, banking industry, and the public. In view of this, the target audience is the Bank of Zambia, the Bankers Association of Zambia, and the public through an organized forum such as the Economics Association of Zambia. Publication of the study findings will also be made available through a peer reviewed journal and the AERC working paper series.

28 | P a g e

APPENDICES

APPENDIX A: LAG LENGTH SELECTION CRITERIA

A1. Policy Rate to Interbank Rate

Lag LogL LR FPE AIC SC HQ

0 -4420.499 NA 1.37e+32 99.53931 99.79097 99.64075 1 -3720.987 1241.830 1.27e+26 85.64017 88.15677* 86.65454* 2 -3607.781 178.0776 6.49e+25* 84.91643 89.69796 86.84373 3 -3525.582 112.6770 7.26e+25 84.88949 91.93596 87.72972 4 -3437.390 103.0558* 8.20e+25 84.72787* 94.03928 88.48103

* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion

A2. Interbank to Commercial Banks Average Lending and Deposit Rates

Lag LogL LR FPE AIC SC HQ

0 -411.0131 NA 36.79793 9.281194 9.337118 9.303735 1 -234.5112 341.1048 0.762650 5.404747 5.572520 5.472371 2 -219.4121 28.50167 0.594409 5.155329 5.434951* 5.268036 3 -211.5339 14.51715 0.545024 5.068178 5.459649 5.225969 4 -204.2952 13.01346* 0.507139* 4.995397* 5.498717 5.198271*

* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion

29 | P a g e

SC: Schwarz information criterion

APPENDIX B. JOHANSEN COINTEGRATION TEST

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.238419 27.52570 15.49471 0.0005 At most 1 0.032928 3.013453 3.841465 0.0826

Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.238419 24.51225 14.26460 0.0009 At most 1 0.032928 3.013453 3.841465 0.0826

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.171906 18.85193 18.39771 0.0432 At most 1 0.018364 1.686686 3.841465 0.1940

Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

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Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.171906 17.16525 17.14769 0.0497 At most 1 0.018364 1.686686 3.841465 0.1940

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level

APPENDIX C: LONG-RUN AND SHORT-RUN PASSTHROUGH RESULTS

Policy Rate to Interbank Rate

1 Cointegrating Equation(s): Log likelihood -56.69456

Normalized cointegrating coefficients (standard error in parentheses) PR IB 1.000000 -1.639349 (0.19382)

Coefficient Std. Error t-Statistic Prob.

ECM1 (-1) -0.394252 0.089351 -4.412397 0.0000 D(IBR (-1)) 0.305382 0.101438 3.010523 0.0035 D(IBR (-2)) 0.121466 0.106434 1.141230 0.2573 D(IBR (-3)) 0.211781 0.098885 2.141689 0.0353 D(IBR (-4)) 0.047914 0.101736 0.470964 0.6390 D(PR (-1)) 0.879108 0.307615 2.857818 0.0055 D(PR (-2)) 1.155340 0.318993 3.621832 0.0005 D(PR (-3)) 0.235743 0.345206 0.682907 0.4967 D(PR (-4)) -0.302106 0.352688 -0.856581 0.3943 Constant -0.024149 0.143813 -0.167923 0.8671

R-squared 0.483354 Mean dependent var 0.035227 Adjusted R-squared 0.423741 S.D. dependent var 1.772022 S.E. of regression 1.345173 Akaike info criterion 3.537567 Sum squared resid 141.1402 Schwarz criterion 3.819083 Log likelihood -145.6529 Hannan-Quinn criter. 3.650983 F-statistic 8.108186 Durbin-Watson stat 1.990116 Prob(F-statistic) 0.000000

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Interbank Rate and Deposit Rate

1 Cointegrating Equation(s): Log likelihood -67.99756

Normalized cointegrating coefficients (standard error in parentheses) DR IB 1.000000 -0.495825 (0.07352)

Coefficient Std.Error t-value t-prob

DDR_1 0.196593 0.09774 2.01 0.0476 DIB_4 0.0392376 0.01679 2.34 0.0219 DTBR_1 0.0744499 0.03395 2.19 0.0312 DTBR_4 0.0653057 0.03227 2.02 0.0462 ECMDR -0.188359 0.04843 -3.89 0.0002 Sigma= 0.27064 RSS = 6.0061543 log-likelihood = -7.16678 no. of observations=87 Mean (DDR)= 0.0375203 se(DDR)=0.340112

AR 1-6 test: F(6,76) = 1.2224 [0.3043] ARCH 1-6 test: F(6,75) = 0.58924 [0.7379] Normality test: Chi^2(2) = 5.9592 [0.0508] Hetero test: F(10,76) = 0.77216 [0.6549] Hetero-X test: F(20,66) = 0.70737 [0.8046] RESET23 test: F(2,80) = 1.9147 [0.1541]

Interbank Rate and Lending Rate

1 Cointegrating Equation(s): Log likelihood -256.2873

Normalized cointegrating coefficients (standard error in parentheses) LR INT @TREND(12M05) 1.000000 -0.636977 -0.105270 (0.07872) (0.01328)

Coefficient Std.Error t-value t-prob Constant 0.121498 0.05098 2.38 0.0195 DIB_3 -0.0675182 0.03205 -2.11 0.0383 DRR_1 0.194694 0.04776 4.08 0.0001 ECMLR1 -0.0879608 0.01686 -5.22 0.0000 Sigma= 0.465875 RSS=17.3631472 R^2 =0.365418 F(3,80) = 15.36 [0.000]** Adj.R^2 = 0.341621 log-likelihood=-52.9792 no. of observations=84 no. of parameters=4 mean(DLR)=0.141421 se(DLR)=0.574158

AR 1-5 test: F(5,75) = 0.74427 [0.5928]

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ARCH 1-5 test: F(5,74) = 0.32581 [0.8959] Normality test: Chi^2(2) = 29.447 [0.0000]** Hetero test: F(6,77) = 0.77566 [0.5915] Hetero-X test: F(9,74) = 0.57905 [0.8102] RESET23 test: F(2,78) = 2.7786 [0.0683]

APPENDIX D: ASYMMETRIC RESULTS

Lending rates

Coefficient Std.Error t-value t-prob DLR_1 0.0797785 0.1116 0.715 0.4770 Constant 0.506704 0.3205 1.58 0.1179 LR_1 -0.0176347 0.01402 -1.26 0.2121 DECLneg (-1) -0.356284 0.2103 -1.69 0.0942 DECLpos (-1) 0.305173 0.1925 1.58 0.1170 Sigma= 0.558456 RSS= 24.6380172 R^2=0.0757528 F(4,79) =1.619 [0.178] log-likelihood= -67.6767 mean(DLR)=0.116961 se(DLR)=0.566722

AR 1-5 test: F(5,74) = 3.8854 [0.0035]** ARCH 1-5 test: F(5,74) = 2.1947 [0.0638] Normality test: Chi^2(2) = 23.513 [0.0000]** Hetero test: F(8,75) = 1.0357 [0.4173] Hetero-X test: F(14,69) = 0.71271 [0.7544] RESET23 test: F(2,77) = 1.7253 [0.1849]

Deposit Rate

Coefficient Std.Error t-value t-prob DDR_1 0.362510 0.1025 3.54 0.0007 Constant 0.320096 0.1802 1.78 0.0792 DR_1 -0.0308878 0.01849 -1.67 0.0985 DECDpos (-1) 0.0937265 0.04786 1.96 0.0535 DECDneg (-1) -0.193679 0.08895 -2.18 0.0323 Sigma=0.310293 RSS= 8.08764576 R^2 =0.187139 F(4,84) =4.835 [0.001]** Adj.R^2=0.148431 log-likelihood= -19.5612 no. of observations=89 no. of parameters=5 mean(DDR)=0.0371828 se(DDR)= 0.336249

AR 1-6 test: F(6,78) = 2.0410 [0.0700] ARCH 1-6 test: F(6,77) = 0.36765 [0.8973] Normality test: Chi^2(2) = 8.5913 [0.0136]* Hetero test: F(8,80) = 0.56109 [0.8066] Hetero-X test: F(14,74) = 0.73894 [0.7289] RESET23 test: F(2,82) = 0.42082 [0.6579]

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