Optimal Decision Rules for Weak GMM: Isaiah Andrews (Harvard)

Isaiah Andrews from Harvard University will present Optimal Decision Rules for Weak GMM.

Abstract: This paper derives the limit experiment for nonlinear GMM models with weak and partial identification. We propose a theoretically-motivated class of default priors on a nonparametric nuisance parameter. These priors imply computationally tractable Bayes decision rules in the limit problem, while leaving the prior on the structural parameter free to be selected by the researcher. We further obtain quasi-Bayes decision rules as the limit of procedures in this class, and derive weighted average power-optimal identification-robust frequentist tests. Finally, we prove a Bernstein-von Mises-type result for the quasi-Bayes posterior under weak and partial identification.

You can read the full paper at this link.