Some Estimation Methods for Dynamic Panel Data Models

Transcription

1 Cairo University Institute of Statistical Studies and Research Department of Applied Statistics and Econometrics Some Estimation Methods for Dynamic Panel Data Models By Mohamed Reda Sobhi Abonazel Assistant Lecturer at Dept. of Applied Statistics and Econometrics Supervised by Prof. Ahmed Hassen Youssef Professor of Applied Statistics Dept. of Applied Statistics and Econometrics Dr. Ahmed Amin El-sheikh Assoc. Prof. of Applied Statistics Dept. of Applied Statistics and Econometrics A Thesis Submitted to the Department of Applied Statistics and Econometrics In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Statistics 2014

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3 Table of Contents List of Abbreviations Acknowledgments iv vi Chapter 1 Introduction The Main Objective of Our Study Summary of the Thesis 2 Chapter 2 Various Estimators for Dynamic Panel Data Models Introduction to Dynamic Panel Data Models Least Squares Estimation Minimum Distance and Maximum Likelihood 9 Estimation Anderson and Hsiao Study Chamberlain Study Instrumental Variables Estimation Use of the Lagged Levels as Instruments Use of the Lagged Differences as Instruments GMM Estimation Arellano-Bond Estimator Keane-Runkle Estimator Arellano-Bover Estimator Ahn-Schmidt Estimator Blundell-Bond Estimator Alvarez-Arellano Estimator Recent Developments and Applications for DPD Models 29 -i-

4 Chapter 3 Bias-Correction Methods for LSDV and GMM Estimators The Asymptotic Bias for LSDV Estimator Bias-Corrected LSDV Estimators Kiviet Estimator Hansen Estimator Bun-Carree Estimator The Asymptotic Bias for GMM Estimators The AR(1) Panel Model and GMM Estimators Small Sample Bias Properties of GMM Estimators Bias-Corrected GMM Estimators 56 Chapter 4 Improving the Efficiency of GMM Estimators The Asymptotic Variance of GMM Estimator The Optimal Weighting matrix for First-Difference 60 GMM Estimator 4.3 The Optimal Weighting matrix for Level GMM 63 Estimator 4.4 New Suboptimal Weighting Matrices for System GMM 66 Estimator 4.5 Efficiency Comparisons for Level and System GMM 70 Estimators 4.6 New Level and System GMM Estimators The Weighted level GMM Estimator The Weighted System GMM Estimators 79 Chapter 5 Monte Carlo Simulation Design of the Simulation The Simulation Results The Results of level GMM Estimators The Results of system GMM Estimators 87 -ii-

5 5.2.3 Performance Analysis of the Variance Ratio Estimator Concluding Remarks 92 Appendix (A) Tables 94 Appendix (B) Figures 107 Appendix (C) Codes of Programs 111 References 117 Arabic Summary -iii-

6 List of Abbreviations 2SLS AR(1) CVE DIF DIF1 DIF2 DPD FE GLS GMM IV KI LEV LEV1 LEV2 LIML LS LSDV Two Stage Least Squares First-Order Autoregressive Covariance Estimator First-Difference GMM One-Step DIF Two-Step DIF Dynamic Panel Data Fixed Effects Generalized Least Squares Generalized Method of Moments Instrumental Variables Kantorovich Inequality Level GMM One-Step LEV Two-Step LEV Limited Information Maximum Likelihood Least Squares Least Squares Dummy Variables -iv-

7 MD ML OLS QML RMSE SYS SYS1 SYS2 WCJSYS1 WCJSYS2 WCSYS1 WCSYS2 WG WJSYS1 WJSYS2 WLEV1 WLEV2 Minimum Distance Maximum Likelihood Ordinary Least Squares Quasi-Maximum Likelihood Root Mean Squared Error System GMM One-Step SYS Two-Step SYS One-Step Weighted (with CJ) SYS Two-Step Weighted (with CJ) SYS One-Step Weighted (with C) SYS Two-Step Weighted (with C) SYS Within Group One-Step Weighted (with J) SYS Two-Step Weighted (with J) SYS Optimal One-Step Weighted LEV Optimal Two-Step Weighted LEV -v-

8 Acknowledgments I m greatly indebted to prof. Ahmed Hassen, professor of applied statistics, dept. of applied statistics and econometrics, Institute of Statistical Studies and Research, for his valuable and generous assistance. My sincere thanks are also dedicated to his for this constructive guidance and warm encouragement throughout the preparation of this thesis. Dr. Ahmed El-sheikh, associate professor of applied statistics, dept. of applied statistics and econometrics, Institute of Statistical Studies and Research, deserves my deepest gratitude and appreciation for his kind supervision, continuous help and active discussions during the preparation of this thesis. I would like to express my thanks to prof. Sayed Mesheal, professor of applied Statistics, dean of Institute of Statistical Studies and Research, for his continuous help and his generous acceptance of discussion of this thesis, and to prof. Amr Elatraby, professor of statistics, vice dean of faculty of commerce, Ain Shams University, for his generous acceptance of discussion of this thesis. -vi-