TY - JOUR
T1 - Measuring prior sensitivity and prior informativeness in large Bayesian models
AU - Mueller, Ulrich K.
PY - 2012/10/1
Y1 - 2012/10/1
N2 - 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).
AB - 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).
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U2 - 10.1016/j.jmoneco.2012.09.003
DO - 10.1016/j.jmoneco.2012.09.003
M3 - Article
AN - SCOPUS:84868302309
SN - 0304-3932
VL - 59
SP - 581
EP - 597
JO - Carnegie-Rochester Confer. Series on Public Policy
JF - Carnegie-Rochester Confer. Series on Public Policy
IS - 6
ER -