Bias-corrected method of moments estimators for dynamic panel data models

Jörg Breitung, Sebastian Kripfganz and Kazuhiko Hayakawa

Econometrics and Statistics, 2022, vol. 24, issue C, 116-132

Abstract: A computationally simple bias correction for linear dynamic panel data models is proposed and its asymptotic properties are studied when the number of time periods is fixed or tends to infinity with the number of panel units. The approach can accommodate both fixed-effects and random-effects assumptions, heteroskedastic errors, as well as higher-order autoregressive models. Panel-corrected standard errors are proposed that allow for robust inference in dynamic models with cross-sectionally correlated errors. Monte Carlo experiments suggest that under the assumption of strictly exogenous regressors the bias-corrected method of moment estimator outperforms popular GMM estimators in terms of efficiency and correctly sized tests.

Keywords: Bias correction; Moment conditions; Autoregressive model; Panel data; Fixed effects; Random Effects (search for similar items in EconPapers)
JEL-codes: C13 C23 C63 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:24:y:2022:i:c:p:116-132

DOI: 10.1016/j.ecosta.2021.07.001

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