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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, ?, ?,, 2021 DOI: 10.1016/j.jeconom.2020.09.010

    Accepted author manuscript, 350 KB, PDF document

    Embargo ends: 13/02/23

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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A Doubly Corrected Robust Variance Estimator for Linear GMM

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
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<mark>Journal publication date</mark>13/02/2021
<mark>Journal</mark>Journal of Econometrics
Volume0
Number of pages23
Pages (from-to)0-0
Publication StatusE-pub ahead of print
Early online date13/02/21
<mark>Original language</mark>English

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.

Bibliographic note

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, ?, ?,, 2021 DOI: 10.1016/j.jeconom.2020.09.010