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Valid Two-Step Identification-Robust Confidence Sets for GMM

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  • Isaiah Andrews

    (MIT)

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.

Suggested Citation

  • Isaiah Andrews, 2018. "Valid Two-Step Identification-Robust Confidence Sets for GMM," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 337-348, May.
    • Handle: RePEc:tpr:restat:v:100:y:2018:i:2:p:337-348
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