Measuring prior sensitivity and prior informativeness in large Bayesian models
成果类型：
Article
署名作者：
Mueller, Ulrich K.
署名单位：
Princeton University
刊物名称：
JOURNAL OF MONETARY ECONOMICS
ISSN/ISSBN：
0304-3932
DOI：
10.1016/j.jmoneco.2012.09.003
发表日期：
2012
页码：
581-597
关键词：
摘要：
In large Bayesian models, such as modern DSGE models, it is difficult to assess how much the prior affects the results. This paper derives measures of prior sensitivity and prior informativeness that account for the high dimensional interaction between prior and likelihood information. The basis for both measures is the derivative matrix of the posterior mean with respect to the prior mean, which is easily obtained from Markov Chain Monte Carlo output. We illustrate the approach by examining posterior results in the small model of Lubik and Schorfheide (2004) and the large model of Smets and Wouters (2007). (C) 2012 Elsevier B.V. All rights reserved.
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