On Gaussian Process Priors in Conditional Moment Restriction Models

Sid Kankanala

Papers from arXiv.org

Abstract: This paper studies quasi Bayesian estimation and uncertainty quantification for an unknown function that is identified by a nonparametric conditional moment restriction. We derive contraction rates for a class of Gaussian process priors. Furthermore, we provide conditions under which a Bernstein von Mises theorem holds for the quasi-posterior distribution. As a consequence, we show that optimally weighted quasi-Bayes credible sets have exact asymptotic frequentist coverage.

Date: 2023-11, Revised 2023-11
New Economics Papers: this item is included in nep-ecm
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