Global Robust Bayesian Analysis in Large Models
Global Robust Bayesian Analysis in Large Models
复制标题
DOI：
10.2139/ssrn.3452643
复制
发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
P. Ho
中科院分区：
文献类型：
--
作者：
P. Ho
本文开发了大型贝叶斯模型的全局先验灵敏度分析工具。在没有施加参数限制的情况下，该框架提供了广泛的后验统计的界限，给定在相对熵方面接近原始的任何先验。该方法还揭示了对感兴趣的后验统计重要的先验部分。为了在大型模型中实现这些计算，我们开发了一种顺序蒙特卡罗算法，并使用对感兴趣的可能性和统计量的近似。我们使用该框架研究了Smets和Wouters(2007)的新凯恩斯模型中产出对货币政策冲击的脉冲响应的误差范围。误差带不对称地依赖于似然的先验特征，如果没有这种正式的先验灵敏度分析，则很难检测到这些特征。
This paper develops tools for global prior sensitivity analysis in large Bayesian models. Without imposing parametric restrictions, the framework provides bounds for a wide range of posterior statistics given any prior that is close to the original in relative entropy. The methodology also reveals parts of the prior that are important for the posterior statistics of interest. To implement these calculations in large models, we develop a sequential Monte Carlo algorithm and use approximations to the likelihood and statistic of interest. We use the framework to study error bands for the impulse response of output to a monetary policy shock in the New Keynesian model of Smets and Wouters (2007). The error bands depend asymmetrically on the prior through features of the likelihood that are hard to detect without this formal prior sensitivity analysis.