Isaiah Andrews, Anna Mikusheva
arXiv 8 Jul 2020 · Econometrics · publishedEconometrica (2022) · 16 citations (OpenAlex)
arXiv:2007.04050 · PDF · DOI · OpenAlex · Extracted main text
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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