标题： 关于条件矩约束模型中的高斯过程先验 显示英文标题

标题： On Gaussian Process Priors in Conditional Moment Restriction Models

摘要： 本文研究由非参数条件矩模型识别的未知函数的准贝叶斯估计和不确定性量化。我们推导了一类高斯过程先验的收缩率，并提供了准后验分布满足伯恩斯坦-冯米塞斯定理的条件。作为结果，我们证明了最优加权准贝叶斯可信集具有精确的渐近频率覆盖性。这扩展了关于参数广义矩方法（GMM）模型中最优加权准贝叶斯可信集的频率有效性经典结果。 显示英文摘要

摘要： This paper studies quasi-Bayesian estimation and uncertainty quantification for an unknown function that is identified by a nonparametric conditional moment restriction model. We derive contraction rates for a class of Gaussian process priors and 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. This extends classical result on the frequentist validity of optimally weighted quasi-Bayes credible sets for parametric generalized method of moments (GMM) models.