Valid Two-Step Identification-Robust Confidence Sets for GMM
Author(s)Andrews, Isaiah; Andrews, Isaiah Smith
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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.
The Review of Economics and Statistics
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
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