Bayesian inference in a class of partially identified models
Bayesian inference in a class of partially identified models
复制标题
一类部分识别模型中的贝叶斯推理
DOI：
10.3982/qe399
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发表时间：
2016
影响因子：
1.8
通讯作者：
E. Tamer
中科院分区：
文献类型：
--
作者：
Brendan Kline;E. Tamer
本文发展了一种贝叶斯方法来推断一类部分识别的计量经济模型。这类模型的特征在于一个点识别的简化形式参数μ和一个部分识别的参数θ的识别集之间的已知映射。该方法将关于μ的后验推理映射到关于θ的已识别集合的各种后验推理陈述，而无需指定θ的先验。考虑了许多后验推理陈述，包括特定参数值（或参数值的集合）在所识别的集合中的后验概率。该方法也适用于θ的函数。本文开发的一般结果大样本近似，这说明了如何确定的数据集的后验概率进行修订，并建立条件下，贝叶斯可信集也是有效的频率置信集。该方法即使在高维模型中也具有计算吸引力，因为该方法避免了在参数空间上的穷举搜索。该方法的性能说明通过蒙特卡洛实验和经验应用到一个二进制条目游戏涉及航空公司。
This paper develops a Bayesian approach to inference in a class of partially identified econometric models. Models in this class are characterized by a known mapping between a point identified reduced‐form parameter μ and the identified set for a partially identified parameter θ. The approach maps posterior inference about μ to various posterior inference statements concerning the identified set for θ, without the specification of a prior for θ. Many posterior inference statements are considered, including the posterior probability that a particular parameter value (or a set of parameter values) is in the identified set. The approach applies also to functions of θ. The paper develops general results on large sample approximations, which illustrate how the posterior probabilities over the identified set are revised by the data, and establishes conditions under which the Bayesian credible sets also are valid frequentist confidence sets. The approach is computationally attractive even in high‐dimensional models, in that the approach avoids an exhaustive search over the parameter space. The performance of the approach is illustrated via Monte Carlo experiments and an empirical application to a binary entry game involving airlines.