Robust standard errors in transformed likelihood estimation of dynamic panel data models with cross-sectional heteroskedasticity

Kazuhiko Hayakawa and Mohammad Pesaran

Journal of Econometrics, 2015, vol. 188, issue 1, 111-134

Abstract: This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao et al. (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-specified 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 finite 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 (search for similar items in EconPapers)
JEL-codes: C12 C13 C23 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:188:y:2015:i:1:p:111-134

DOI: 10.1016/j.jeconom.2015.03.042

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