Prior Density-Ratio Class Robustness in Econometrics
提出一种通用且快速的方法，利用后验模拟器的输出精确计算后验期望的密度比类别边界，并在一个典型复杂度的计量模型中展示应用，发现某些函数期望的精确边界与渐近近似吻合良好，而其他则不然。
Abstract This article provides a generic, very fast method for computing exact density-ratio class bounds on posterior expectations, given the output of a posterior simulator. It illustrates application of the method in an econometric model of typical complexity. In this model, the exact bounds for expectations of some functions of interest are well approximated by the established asymptotic approximation, but others are not. Software for the computations is publicly available in a variety of programming languages. KEY WORDS: Bayesian inferenceMarkov-chain Monte CarloNormal mixtureProbit model