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  • HKL_Sep8-2020-final_accepted

    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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    Embargo ends: 13/02/23

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A Doubly Corrected Robust Variance Estimator for Linear GMM

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print

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A Doubly Corrected Robust Variance Estimator for Linear GMM. / Hwang, Jungbin; Kang, David; Lee, Seojeong.

In: Journal of Econometrics, Vol. 229, No. 2, 31.08.2022, p. 276-298.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Hwang, J, Kang, D & Lee, S 2022, 'A Doubly Corrected Robust Variance Estimator for Linear GMM', Journal of Econometrics, vol. 229, no. 2, pp. 276-298. https://doi.org/10.1016/j.jeconom.2020.09.010

APA

Vancouver

Author

Hwang, Jungbin ; Kang, David ; Lee, Seojeong. / A Doubly Corrected Robust Variance Estimator for Linear GMM. In: Journal of Econometrics. 2022 ; Vol. 229, No. 2. pp. 276-298.

Bibtex

@article{4f8285a714a54c2a8cf6f95ce8d409cc,
title = "A Doubly Corrected Robust Variance Estimator for Linear GMM",
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",
author = "Jungbin Hwang and David Kang and Seojeong Lee",
note = "This is the author{\textquoteright}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",
year = "2021",
month = feb,
day = "13",
doi = "10.1016/j.jeconom.2020.09.010",
language = "English",
volume = "229",
pages = "276--298",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "2",

}

RIS

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 - 2021/2/13

Y1 - 2021/2/13

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 -