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Many of our seminars and public events this year will continue as in person or as hybrid (online and in person) events.

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Unless otherwise specified, in-person seminars are open to the public. Please ensure you have informed the event contact as early as possible.

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About this event

This paper develops a generalized Bayes framework for conditional moment restriction models, where the parameter of interest is a nonparametric structural function of endogenous variables. We establish contraction rates for a class of Gaussian process priors and provide conditions under which a Bernstein-von Mises theorem holds for the quasi-Bayes posterior. Consequently, we show that optimally weighted quasi-Bayes credible sets achieve exact asymptotic frequentist coverage, extending classical results for parametric GMM models.