{"ID":2862169,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.01036","arxiv_id":"2510.01036","title":"Generalized Bayes in Conditional Moment Restriction Models","abstract":"This paper develops a generalized (quasi-) 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. As an application, we estimate firm-level production functions using Chilean plant-level data. Simulations illustrate the favorable performance of generalized Bayes estimators relative to common alternatives.","short_abstract":"This paper develops a generalized (quasi-) 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 M...","url_abs":"https://arxiv.org/abs/2510.01036","url_pdf":"https://arxiv.org/pdf/2510.01036v1","authors":"[\"Sid Kankanala\"]","published":"2025-10-01T15:40:55Z","proceeding":"econ.EM","tasks":"[\"econ.EM\",\"math.ST\"]","methods":"[]","has_code":false}