Breitung, Jörg ORCID: 0000-0001-7367-0863, 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. Econometrics and Statistics, 24. 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:
CreatorsEmailORCIDORCID Put Code
Breitung, JörgUNSPECIFIEDorcid.org/0000-0001-7367-0863UNSPECIFIED
Kripfganz, SebastianUNSPECIFIEDorcid.org/0000-0002-7670-0834UNSPECIFIED
Hayakawa, KazuhikoUNSPECIFIEDorcid.org/0000-0002-8321-8448UNSPECIFIED
URN: urn:nbn:de:hbz:38-674367
DOI: 10.1016/j.ecosta.2021.07.001
Journal or Publication Title: Econometrics and Statistics
Volume: 24
Page Range: 116 - 133
Date: 2022
Publisher: ELSEVIER
Place of Publication: AMSTERDAM
ISSN: 2452-3062
Language: English
Faculty: Faculty of Management, Economy and Social Sciences
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
MAXIMUM-LIKELIHOOD-ESTIMATION; INFERENCEMultiple languages
EconomicsMultiple languages
Refereed: Yes
URI: http://kups.ub.uni-koeln.de/id/eprint/67436

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