摘要翻译：
我们提供了贝叶斯部分辨识计量经济学模型的全面半参数研究。现有的贝叶斯部分辨识文献大多集中在结构参数上，而本文主要研究未知辨识集的贝叶斯可信集及其支持函数的后验分布。我们基于识别集的支持函数构造了一个（双边）BCS。我们证明了支持函数后验分布的Bernstein-von Mises定理。这个有力的结果反过来又推论出，当部分识别参数的BCS和频率置信集渐近不同时，我们构造的识别集的BCS具有渐近正确的频率复盖概率。重要的是，我们说明了对于识别集构造的BCS不需要结构参数的先验。它可以有效地用于子集推理，特别是当目标是部分识别参数的子向量时，通常需要向低维子集投影。因此，所提出的方法在许多应用中都是有用的。贝叶斯部分辨识文献一直假设一个已知的参数似然函数。然而，计量经济学模型通常只识别一组矩不等式，因此使用不正确的似然函数可能会导致误导性的推断。与此相反，在未知似然函数的非参数先验下，我们提出的贝叶斯方法只需要一组矩条件，就可以有效地对部分识别参数及其识别集进行推理。这使得它在一般的矩不等式模型中具有广泛的适用性。最后，在一个金融资产定价问题中说明了所提出的方法。
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英文标题：
《Semi-parametric Bayesian Partially Identified Models based on Support
  Function》
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作者：
Yuan Liao, Anna Simoni
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最新提交年份：
2013
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  We provide a comprehensive semi-parametric study of Bayesian partially identified econometric models. While the existing literature on Bayesian partial identification has mostly focused on the structural parameter, our primary focus is on Bayesian credible sets (BCS's) of the unknown identified set and the posterior distribution of its support function. We construct a (two-sided) BCS based on the support function of the identified set. We prove the Bernstein-von Mises theorem for the posterior distribution of the support function. This powerful result in turn infers that, while the BCS and the frequentist confidence set for the partially identified parameter are asymptotically different, our constructed BCS for the identified set has an asymptotically correct frequentist coverage probability. Importantly, we illustrate that the constructed BCS for the identified set does not require a prior on the structural parameter. It can be computed efficiently for subset inference, especially when the target of interest is a sub-vector of the partially identified parameter, where projecting to a low-dimensional subset is often required. Hence, the proposed methods are useful in many applications.   The Bayesian partial identification literature has been assuming a known parametric likelihood function. However, econometric models usually only identify a set of moment inequalities, and therefore using an incorrect likelihood function may result in misleading inferences. In contrast, with a nonparametric prior on the unknown likelihood function, our proposed Bayesian procedure only requires a set of moment conditions, and can efficiently make inference about both the partially identified parameter and its identified set. This makes it widely applicable in general moment inequality models. Finally, the proposed method is illustrated in a financial asset pricing problem.
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PDF链接：
https://arxiv.org/pdf/1212.3267