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arXiv · 2007.04050

Optimal Decision Rules for Weak GMM

Isaiah Andrews·Anna Mikusheva
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Abstract

This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically-motivated class of priors which give rise to quasi-Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi-Bayes approach regardless of model identification status, and we recommend quasi-Bayes for settings where identification is a concern. We further propose weighted average power-optimal identification-robust frequentist tests and confidence sets, and prove a Bernstein-von Mises-type result for the quasi-Bayes posterior under weak identification.

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Isaiah Andrews, Anna Mikusheva. 2020-07-08. Optimal Decision Rules for Weak GMM. https://arxiv.org/abs/2007.04050

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