Rights statement: 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
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A Doubly Corrected Robust Variance Estimator for Linear GMM
AU - Hwang, Jungbin
AU - Kang, David
AU - Lee, Seojeong
N1 - 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
PY - 2022/8/31
Y1 - 2022/8/31
N2 - 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.
AB - 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.
KW - Generalized method of moments
KW - Variance correction
KW - Panel data
KW - Model misspecification
U2 - 10.1016/j.jeconom.2020.09.010
DO - 10.1016/j.jeconom.2020.09.010
M3 - Journal article
VL - 229
SP - 276
EP - 298
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 2
ER -