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Title:Robust standard errors in transformed likelihood estimation of dynamic panel data models PDF Logo
Authors:Hayakawa, Kazuhiko
Pesaran, M. Hashem
Issue Date:2012
Series/Report no.:Discussion Paper series, Forschungsinstitut zur Zukunft der Arbeit 6583
Abstract: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.
Subjects:dynamic panels
cross-sectional heteroskedasticity
Monte Carlo simulation
GMM estimation
JEL:C12
C13
C23
Persistent Identifier of the first edition:urn:nbn:de:101:1-200911022246
Document Type:Working Paper
Appears in Collections:IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)

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