Journal
JOURNAL OF ECONOMETRICS
Volume 229, Issue 2, Pages 276-298Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2020.09.010
Keywords
Generalized method of moments; Variance correction; Panel data; Model misspecification
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Funding
- Department of Economics at University of Connecticut, United States
- Lancaster University Management School, United Kingdom [ECA6396]
- Australian Research Council Discovery Early Career Research Award (DECRA) funding scheme [DE170100787]
- Australian Research Council [DE170100787] Funding Source: Australian Research Council
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This paper proposes a new finite sample corrected variance estimator for linear generalized method of moments (GMM), which can correct the bias from estimating the efficient weight matrix and the over-identification bias in variance estimation. It also automatically provides robustness to misspecification of the moment condition, improving inference under correct specification and robustness against misspecification.
We propose a new finite sample corrected variance estimator for the linear generalized method of moments (GMM) including the one-step, two-step, and iterated estimators. Our formula also corrects the over-identification bias in variance estimation on top of the commonly used finite sample correction of Windmeijer (2005), which corrects the bias from estimating the efficient weight matrix, so is doubly corrected. An important feature of the proposed double correction is that it automatically provides robustness to misspecification of the moment condition. In contrast, the conventional variance estimator and the Windmeijer correction are inconsistent under misspecification. That is, the double correction formula proposed in this paper provides a convenient way to obtain improved inference under correct specification and robustness against misspecification at the same time. (C) 2021 Elsevier B.V. All rights reserved.
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