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Measuring prior sensitivity and prior informativeness in large Bayesian models

  • Müller, Ulrich K.
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    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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    File URL: http://www.sciencedirect.com/science/article/pii/S030439321200092X
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    Article provided by Elsevier in its journal Journal of Monetary Economics.

    Volume (Year): 59 (2012)
    Issue (Month): 6 ()
    Pages: 581-597

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    Handle: RePEc:eee:moneco:v:59:y:2012:i:6:p:581-597
    Contact details of provider: Web page: http://www.elsevier.com/locate/inca/505566

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    1. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, 05.
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