Measuring prior sensitivity and prior informativeness in large Bayesian models

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Abstract

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).

Original languageEnglish (US)
Pages (from-to)581-597
Number of pages17
JournalJournal of Monetary Economics
Volume59
Issue number6
DOIs
StatePublished - Oct 1 2012

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

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