A Doubly Corrected Robust Variance Estimator for Linear GMM

Hwang, Jungbin and Kang, David and Lee, Seojeong (2022) A Doubly Corrected Robust Variance Estimator for Linear GMM. Journal of Econometrics, 229 (2). pp. 276-298. ISSN 0304-4076

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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Econometrics
Additional Information:
This is the author’s version of a work that was accepted for publication in Journal of Econometrics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Econometrics, 229, 2, 2022 DOI: 10.1016/j.jeconom.2020.09.010
Uncontrolled Keywords:
?? generalized method of momentsvariance correctionpanel datamodel misspecificationeconomics and econometricssocial sciences (miscellaneous)history and philosophy of scienceapplied mathematicsc12c13c23c26 ??
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Deposited On:
16 Oct 2020 10:05
Last Modified:
12 Jan 2024 00:20