Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models with Cross-Sectional Heteroskedasticity

This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao, Pesaran, and Tahmiscioglu (2002) to the case where the errors are cross-sectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem and its implications for estimation and inference. We approach the problem by working with a mis-speci…ed homoskedastic model, and then show that the transformed maximum likelihood estimator continues to be consistent even in the presence of cross-sectional heteroskedasticity. We also obtain standard errors that are robust to cross-sectional heteroskedasticity of unknown form. By means of Monte Carlo simulations, we investigate the …nite sample behavior of the transformed maximum likelihood estimator and compare it with various GMM estimators proposed in the literature. Simulation results reveal that, in terms of median absolute errors and accuracy of inference, the transformed likelihood estimator outperforms the GMM estimators in almost all cases. Keywords: Dynamic Panels, Cross-sectional heteroskedasticity, Monte Carlo simulation, Transformed MLE, GMM estimation JEL Codes: C12, C13, C23 We are grateful to three referees and the participants at the 18th International Conference on Panel Data, and seminars at Osaka, Sogang and Nanyang Technological University for helpful comments. This paper was written whilst Hayakawa was visiting the University of Cambridge as a JSPS Postdoctoral Fellow for Research Abroad. He acknowledges the …nancial support from the JSPS Fellowship and the Grant-in-Aid for Scienti…c Research (KAKENHI 22730178) provided by the JSPS. Pesaran acknowledges …nancial support from the ESRC Grant No. ES/1031626/1. Elisa Tosetti contributed to a preliminary version of this paper. Her assistance in coding of the transformed ML estimator and some of the derivations is gratefully acknowledged.