Valid Two-Step Identification-Robust Confidence Sets for GMM

In models with potential 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 one when the model is wellidentified. JEL Classification: C12, C18, C26