Panel Data Analysis With Special Application to Monetary Policy Transmission Mechanism User Guide Panel Data Analysis With Special Application to Monetary Policy Transmission Mechanism Prepared By Dr Esman Nyamongo Assistant Director Research Department Central Bank of Kenya Published by COMESA Monetary Institute (CMI) First Published 2019 by COMESA Monetary Institute C/O Kenya School of Monetary Studies P.O. Box 65041 – 00618 Noordin Road Nairobi, KENYA Tel: +254 – 20 – 8646207 http://cmi.comesa.int Copyright © 2019, COMESA Monetary Institute (CMI) All rights reserved. Except for fully acknowledged short citations for purposes of research and teaching, no part of this publication may be reproduced or transmitted in any form or by any means without prior permission from COMESA. Disclaimer The views expressed herein are those of the author and do not in any way represent the official position of COMESA, its Member States, or the affiliated Institution of the Author. Typesetting and Design Mercy W. Macharia [email protected] TABLE OF CONTENTS List of Figures ............................................................................................ viii List of Tables ............................................................................................. viii List of Acronyms ........................................................................................ ix Preface ........................................................................................................... x Acknowledgements ..................................................................................... xi 1. INTRODUCTION TO PANEL DATA ANALYSIS ...................1 1.0 Introduction.......................................................................................... 1 1.1 Types of Panel Data ............................................................................ 1 1.1.1 Dated vs. Undated Panels ..................................................................... 2 1.1.2 Regular vs. Irregular Dated Panels ........................................................ 2 1.1.3 Balanced vs. Unbalanced Panels ............................................................ 2 1.2 Advantages of Panel Data .................................................................. 4 2. GETTING STARTED IN EVIEWS SOFTWARE ..................... 9 2.0 Introduction.......................................................................................... 9 2.1 Getting Started in Eviews ................................................................... 9 2.2 Data preparation in Excel ................................................................. 11 2.3 To Create an Eviews Workfile ......................................................... 11 2.3.1 Importing the data into Eviews ............................................................ 12 2.3.2 Setting up a pool in a workfile ............................................................. 14 2.3.3 Data transformations .......................................................................... 19 2.4 Viewing Data ...................................................................................... 21 2.5 Basic Plots ........................................................................................... 22 2.6 Descriptive Statistics ......................................................................... 24 3. POOLED REGRESSION ANALYSIS ................................... 29 3.0 Introduction ........................................................................................ 29 3.1 The pooled regression model ........................................................... 29 3.1.1 Limitations of Pooled regression ........................................................... 30 3.2 Estimation of the Pooled Regression Model ................................. 31 3.2.1 Illustration of pooled regression using general data ................................ 31 3.2.2 Organising data in Excel .................................................................... 32 3.2.3 Loading the data into Eviews .............................................................. 34 3.2.4 Pooled regression in Eviews.................................................................. 36 3.2.5 Pooled regressions in Eviews Environment ........................................... 37 3.3 Application of Pooled Regression Approach to Monetary Policy Transmission in Kenya .......................................................... 39 3.3.1 Case Study: Monetary Policy Transmission in Kenya: Evidence from bank level data ............................................................................ 39 3.3.2 The Model setup.................................................................................. 40 References ................................................................................................... 45 4. ERROR COMPONENT MODEL ANALYSIS: ONE WAY ERROR COMPONENTS MODEL ...................................... 47 4.0 Introduction ........................................................................................ 47 4.1 The Error Components Model Specification ................................ 47 4.1.1 One-Way Error Component Model ..................................................... 48 4.1.2 The least squares dummy variable estimation method .......................... 49 4.2 Case Study: Monetary Policy Transmission in Kenya: Evidence from bank level data ......................................................... 57 4.2.1 Within-Q-estimation method .................................................................... 59 4.3 Pooled Estimation Method Versus the Fixed Effect Method................................................................................................. 65 4.4 Case Study: Monetary Policy Transmission in Kenya: Evidence from bank level data ......................................................... 68 ~ vi ~ 4.5 Random Effects Model ..................................................................... 70 4.5.1 Testing the validity of the random effects: Hausman test ............................ 71 References ................................................................................................... 73 5. ERROR COMPONENT MODEL ANALYSIS: TWO WAY ERROR COMPONENTS MODEL ..................................... 75 5.0 Introduction ......................................................................................... 75 5.1 Estimation of the Error Components Model ................................. 76 5.1.1 Fixed effects model.................................................................................... 76 6. DYNAMIC PANEL DATA ANALYSIS ................................ 83 6.1 Arellano and Bond Estimator .......................................................... 84 6.2 Estimation of Dynamic Panel in Eviews ....................................... 85 6.3 Step by Step Implementation of the Dynamic GMM Procedure in Eviews .......................................................................... 86 References ................................................................................................... 95 7. NON-STATIONARY PANEL ANALYSIS ........................... 97 7.0 Panel Unit-root Tests ........................................................................ 97 7.1 Panel Unit-root Test with an Automatic Lag Selection Method .............................................................................................. 105 References ................................................................................................. 107 ~ vii ~ LIST OF FIGURES Figure 1: Creating a work file .............................................................................. 34 Figure 2: Getting the data into Eviews .............................................................. 35 Figure 3: Estimation in a pool object ................................................................. 36 Figure 4: Estimation result .................................................................................. 37 Figure 5: BLC based on pooled regressions analysis ....................................... 44 Figure 6: GMM Model Specification ................................................................. 93 LIST OF TABLES Table 1.1: Panel data set: Normalised bank size for 5 banks ........................... 4 Table 3.1: Raw data on bank size and loan ....................................................... 32 Table 3.2: Stacked data on bank size and loan ................................................. 33 Table 3.3: Data on size and growth rate of loan with cross-section identifiers ............................................................................................. 38 Table 4.1: Dummy variables ................................................................................ 51 Table 4.2: Demeaned data ................................................................................... 61 Table 5.1: Data ...................................................................................................... 77 Table 5.2: transformed data: ................................................................................ 77 Table 6.1: Stacked data on bank size and loan ................................................. 86 Table 6.2: The Estimation results ....................................................................... 94 ~ viii ~ LIST OF ACRONYMS ADF Augmented Dickey-Fuller AIC Akaike Information Criterion BIC Bayesian Information Criteria BLC Bank Lending Channel CBR Central Bank Rate DPD Dynamic Panel Data GDP Gross Domestic