A doubly corrected robust variance estimator for linear GMM

Jungbin Hwang, Byunghoon Kang and Seojeong Lee

Journal of Econometrics, 2022, vol. 229, issue 2, 276-298

Abstract: 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.

Keywords: Generalized method of moments; Variance correction; Panel data; Model misspecification (search for similar items in EconPapers)
JEL-codes: C12 C13 C23 C26 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Working Paper: A Doubly Corrected Robust Variance Estimator for Linear GMM (2020)
Working Paper: A Doubly Corrected Robust Variance Estimator for Linear GMM (2019)
Working Paper: A Doubly Corrected Robust Variance Estimator for Linear GMM (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:229:y:2022:i:2:p:276-298

DOI: 10.1016/j.jeconom.2020.09.010

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