Bayesian and Frequentist Inference in Partially Identified Models
推导了部分识别结构参数后验分布的大样本近似，分析了频率学派置信集与贝叶斯可信集在部分识别模型中的差异，并用矩不等式模型和两玩家进入博弈进行了说明。
A large sample approximation of the posterior distribution of partially identified structural parameters is derived for models that can be indexed by a finite-dimensional reduced form parameter vector.It is used to analyze the differences between frequentist confidence sets and Bayesian credible sets in partially identified models.A key difference is that frequentist set estimates extend beyond the boundaries of the identified set (conditional on the estimated reduced form parameter), whereas Bayesian credible sets can asymptotically be located in the interior of the identified set.Our asymptotic approximations are illustrated in the context of simple moment inequality models and a numerical illustration for a two-player entry game is provided.