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Analysis of variance for bayesian inference

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  • Geweke, John
  • Amisano, Gianni

Abstract

This paper develops a multi-way analysis of variance for non-Gaussian multivariate distributions and provides a practical simulation algorithm to estimate the corresponding components of variance. It specifically addresses variance in Bayesian predictive distributions, showing that it may be decomposed into the sum of extrinsic variance, arising from posterior uncertainty about parameters, and intrinsic variance, which would exist even if parameters were known. Depending on the application at hand, further decomposition of extrinsic or intrinsic variance (or both) may be useful. The paper shows how to produce simulation-consistent estimates of all of these components, and the method demands little additional effort or computing time beyond that already invested in the posterior simulator. It illustrates the methods using a dynamic stochastic general equilibrium model of the US economy, both before and during the global financial crisis. JEL Classification: C11, C53

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 1409.

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Date of creation: Dec 2011
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Handle: RePEc:ecb:ecbwps:20111409

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Related research

Keywords: Analysis of variance; Bayesian inference; posterior simulation; predictive distributions;

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  1. Smets, Frank & Wouters, Rafael, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," CEPR Discussion Papers 6112, C.E.P.R. Discussion Papers.
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