A Doubly Corrected Robust Variance Estimator for Linear GMM

Jungbin Hwang, Byunghoon Kang and Seojeong Lee

No 274731767, Working Papers from Lancaster University Management School, Economics Department

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 additionally corrects for the over-identification bias in variance estimation on top of the commonly used finite sample correction of Windmeijer (2005) which corrects for the bias from estimating the efficient weight matrix, so is doubly corrected. Formal stochastic expansions are derived to show the proposed double correction estimates the variance of some higher-order terms in the expansion. In addition, the proposed double correction 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 proposed double correction formula provides a convenient way to obtain improved inference under correct specification and robustness against misspecification at the same time.

Date: 2019
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: A doubly corrected robust variance estimator for linear GMM (2022)
Working Paper: A Doubly Corrected Robust Variance Estimator for Linear GMM (2020)
Working Paper: A Doubly Corrected Robust Variance Estimator for Linear GMM (2019)
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