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GMM is Inadmissible Under Weak Identification by Andrews, Isaiah; Mikusheva, Anna is a scholarly article available to read on EtoBox.

What is GMM is Inadmissible Under Weak Identification about?

We consider estimation in moment condition models and show that under any bound on identification strength, asymptotically admissible (i.e. undominated) estimators in a wide class of estimation problems must be uniformly continuous in the sample moment function. GMM estimators are in general discontinuous in the sample moments, and are thus inadmissible. We show, by contrast, that bagged, or bootstrap aggregated, GMM estimators as well as quasi-Bayes posterior means have superior continuity properties, while results in the literature imply that they are equivalent to GMM when identification is strong. In simulations calibrated to published instrumental variables specifications, we find that these alternatives often outperform GMM.

Author

Andrews, Isaiah; Mikusheva, Anna

Published

2022

Language

EN