Optimal Decision Rules for Weak GMM
Econometrica, ISSN: 1468-0262, Vol: 90, Issue: 2, Page: 715-748
2022
- 8Citations
- 26Captures
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Article Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85126889488&origin=inward; http://dx.doi.org/10.3982/ecta18678; https://www.econometricsociety.org/doi/10.3982/ECTA18678; https://dx.doi.org/10.3982/ecta18678; https://www.econometricsociety.org/publications/econometrica/2022/03/01/optimal-decision-rules-weak-gmm
The Econometric Society
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