Universität zu Köln

Breitung, Joerg, Kripfganz, Sebastian ORCID: 0000-0002-7670-0834 and Hayakawa, Kazuhiko ORCID: 0000-0002-8321-8448 (2022). Bias-corrected method of moments estimators for dynamic panel data models. Econom. Stat., 24. S. 116 - 133. AMSTERDAM: ELSEVIER. ISSN 2452-3062

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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. (C) 2021 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Breitung, Joerg UNSPECIFIED UNSPECIFIED UNSPECIFIED Kripfganz, Sebastian UNSPECIFIED orcid.org/0000-0002-7670-0834 UNSPECIFIED Hayakawa, Kazuhiko UNSPECIFIED orcid.org/0000-0002-8321-8448 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-674367
DOI:	10.1016/j.ecosta.2021.07.001
Journal or Publication Title:	Econom. Stat.
Volume:	24
Page Range:	S. 116 - 133
Date:	2022
Publisher:	ELSEVIER
Place of Publication:	AMSTERDAM
ISSN:	2452-3062
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language MAXIMUM-LIKELIHOOD-ESTIMATION; INFERENCE Multiple languages Economics Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/67436