Testing initial conditions in dynamic panel data models

Abstract We propose a new framework for testing the “mean stationarity” assumption in dynamic panel data models, required for the consistency of the system GMM estimator. In our set up the assumption is obtained as a parametric restriction in an extended set of moment conditions, allowing the use of a LM test to check its validity. Our framework provides a ranking in terms of power of the analyzed test statistics, in which our approach exhibits better power than the difference-in-Sargan/Hansen test that compares system GMM and difference GMM, that is, on its turn, more powerful than the Sargan/Hansen test based on the system GMM moment conditions.

[1]  Hairul Azlan Annuar,et al.  Foreign investors' interests and corporate tax avoidance: Evidence from an emerging economy , 2015 .

[2]  Z. Griliches.  Patent Statistics as Economic Indicators: a Survey , 1990 .

[3]  Kazuhiko Hayakawa,et al.  On the behaviour of the GMM estimator in persistent dynamic panel data models with unrestricted initial conditions , 2016, Comput. Stat. Data Anal..

[4]  M. Arellano,et al.  Another look at the instrumental variable estimation of error-components models , 1995 .

[5]  L. Hansen,et al.  Finite Sample Properties of Some Alternative Gmm Estimators , 2015 .

[6]  Patrik Guggenberger.  THE IMPACT OF A HAUSMAN PRETEST ON THE ASYMPTOTIC SIZE OF A HYPOTHESIS TEST , 2009, Econometric Theory.

[7]  L. Hansen.  LARGE SAMPLE PROPERTIES OF GENERALIZED METHOD OF , 1982 .

[8]  W. Newey,et al.  Hypothesis Testing with Efficient Method of Moments Estimation , 1987 .

[9]  Christian Gourieroux,et al.  Indirect Inference for Dynamic Panel Models , 2006 .

[10]  Jinyong Hahn,et al.  How informative is the initial condition in the dynamic panel model with fixed effects , 1999 .

[11]  James G. MacKinnon,et al.  Graphical Methods for Investigating the Size and Power of Hypothesis Tests , 1998 .

[12]  Thomas Brox,et al.  Maximum Likelihood Estimation , 2019, Time Series Analysis.

[13]  Whitney K. Newey,et al.  Generalized method of moments with many weak moment conditions , 2009 .

[14]  Peter Schmidt,et al.  Efficient estimation of models for dynamic panel data , 1995 .

[15]  Kazuhiko Hayakawa,et al.  On the effect of mean-nonstationarity in dynamic panel data models , 2009 .

[16]  W. Newey,et al.  Estimating vector autoregressions with panel data , 1988 .

[17]  M. Pesaran,et al.  Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods , 2002 .

[18]  Cheng Hsiao,et al.  Estimation of Dynamic Models with Error Components , 1981 .

[19]  David Roodman,et al.  A Note on the Theme of Too Many Instruments , 2008 .

[20]  Kazuhiko Hayakawa,et al.  Small sample bias properties of the system GMM estimator in dynamic panel data models , 2007 .

[21]  Paul A. Ruud,et al.  An Introduction to Classical Econometric Theory , 2000 .

[22]  F. Windmeijer,et al.  RELIABLE INFERENCE FOR GMM ESTIMATORS? FINITE SAMPLE PROPERTIES OF ALTERNATIVE TEST PROCEDURES IN LINEAR PANEL DATA MODELS , 2005 .

[23]  R. Blundell,et al.  Initial Conditions and Moment Restrictions in Dynamic Panel Data Models , 1998 .

[24]  David Roodman.  How to do Xtabond2: An Introduction to Difference and System GMM in Stata , 2006 .

[25]  Frank Windmeijer,et al.  The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models , 2007 .

[26]  Stephen Bond,et al.  Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator , 2000 .

[27]  W. Newey,et al.  Large sample estimation and hypothesis testing , 1986 .

[28]  Cheng Hsiao,et al.  Formulation and estimation of dynamic models using panel data , 1982 .

[29]  Jan F. Kiviet,et al.  On bias, inconsistency and efficiency of various estimators in dynamic panel data models , 1995 .

[30]  Stephen Bond,et al.  Criterion-Based Inference for GMM in Autoregressive Panel Data Models , 2001 .