Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models

By Kazuhiko Hayakawa, M. Pesaran, 12 WP38. Social Science Research Network, 2015

62 citations recorded by the source corpus.

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 crosssectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem that arises, and its implications for estimation and inference. We approach the problem by working with a mis-specified homoskedastic model. It is shown 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 simulation, 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.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/624cd04270e5c535613f481a64d4e7c369bbc6f5

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In Economics and Econometrics

Citations in the corpus

Quasi–maximum Likelihood Estimation of Linear Dynamic Short-T panel-data Models (2016, methodology): “8) to obtain a concentrated log-likelihood function in terms of the parameter 10As discussed by Hayakawa and Pesaran (2015), the subsequent arguments are sustained if eit is cross-sectionally heteroskedastic with variance σ2 e,i.”
Estimation of linear dynamic panel data models with time‐invariant regressors (2019, background): “26Hayakawa and Pesaran (2012) extend the transformed likelihood estimator to accommodate for heteroscedastic errors.”