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Main Authors: Hwang, Jungbin, Kang, Byunghoon, Lee, Seojeong
Format: Preprint
Published: 2019
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Online Access:https://arxiv.org/abs/1908.07821
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author Hwang, Jungbin
Kang, Byunghoon
Lee, Seojeong
author_facet Hwang, Jungbin
Kang, Byunghoon
Lee, Seojeong
contents 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. 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 proposed double correction formula provides a convenient way to obtain improved inference under correct specification and robustness against misspecification at the same time.
format Preprint
id arxiv_https___arxiv_org_abs_1908_07821
institution arXiv
publishDate 2019
record_format arxiv
spellingShingle A Doubly Corrected Robust Variance Estimator for Linear GMM
Hwang, Jungbin
Kang, Byunghoon
Lee, Seojeong
Econometrics
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. 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 proposed double correction formula provides a convenient way to obtain improved inference under correct specification and robustness against misspecification at the same time.
title A Doubly Corrected Robust Variance Estimator for Linear GMM
topic Econometrics
url https://arxiv.org/abs/1908.07821