Valid Two-Step Identification-Robust Confidence Sets for GMM
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rest_a_00682.pdf
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Author(s)
Andrews, Isaiah
Andrews, Isaiah Smith
Date Issued
June 2017
Journal
The Review of Economics and Statistics
Publisher
MIT Press
Citation
Andrews, Isaiah. “Valid Two-Step Identification-Robust Confidence Sets for GMM.” The Review of Economics and Statistics (June 2017) © 2018 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology
Version
Final published version
Abstract
In models with potentially weak identification, researchers often decide whether to report a robust confidence set based on an initial assessment of model identification. Two-step procedures of this sort can generate large coverage distortions for reported confidence sets, and existing procedures for controlling these distortions are quite limited. This paper introduces a generally applicable approach to detecting weak identification and constructing two-step confidence sets in GMM. This approach controls coverage distortions under weak identification and indicates strong identification, with probability tending to 1 when the model is well identified.
MIT Department
Massachusetts Institute of Technology. Department of Economics
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1162/REST_A_00682
