Optimal Decision Rules for Weak GMM
Name
2007.04050.pdf
Description
Submitted version
Size
1.34 MB
Format
Adobe PDF
Checksum (MD5)
4e8f530e4adf46bda4d53ebee61aa5f1
Author(s)
Andrews, Isaiah
Mikusheva, Anna
Date Issued
2022
Journal
Econometrica
Publisher
The Econometric Society
Citation
Andrews, Isaiah and Mikusheva, Anna. 2022. "Optimal Decision Rules for Weak GMM." Econometrica, 90 (2).
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3982/ECTA18678
