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On Gaussian Process Priors in Conditional Moment Restriction Models

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  • Sid Kankanala

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.

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  • Sid Kankanala, 2023. "On Gaussian Process Priors in Conditional Moment Restriction Models," Papers 2311.00662, arXiv.org, revised Nov 2023.
    • Handle: RePEc:arx:papers:2311.00662
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    1. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
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