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Measuring prior sensitivity and prior informativeness in large Bayesian models

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  • Müller, Ulrich K.

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).

Suggested Citation

  • Müller, Ulrich K., 2012. "Measuring prior sensitivity and prior informativeness in large Bayesian models," Journal of Monetary Economics, Elsevier, vol. 59(6), pages 581-597.
  • Handle: RePEc:eee:moneco:v:59:y:2012:i:6:p:581-597
    DOI: 10.1016/j.jmoneco.2012.09.003
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    3. Winkelried, Diego, 2013. "Modelo de Proyección Trimestral del BCRP: Actualización y novedades," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 26, pages 9-60.
    4. Pengfei Wang & Yi Wen & Zhiwei Xu, 2018. "Financial Development and Long-Run Volatility Trends"," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 28, pages 221-251, April.
    5. repec:wly:jmoncb:v:50:y:2018:i:7:p:1571-1616 is not listed on IDEAS

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