Optimal Decision Rules for Weak GMM

• 成果类型: Article
• 署名作者: Andrews, Isaiah; Mikusheva, Anna
• 署名单位: Harvard University; National Bureau of Economic Research; Massachusetts Institute of Technology (MIT)
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA18678
• 发表日期: 2022
• 页码: 715-748
• 关键词: bayesian-analysis; moment conditions; inference; tests; identification; likelihood

摘要

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.