Analysis of variance for bayesian inference

Gianni Amisano and John Geweke

No 1409, Working Paper Series from European Central Bank

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

Keywords: analysis of variance; Bayesian inference; posterior simulation; predictive distributions (search for similar items in EconPapers)
Date: 2011-12
New Economics Papers: this item is included in nep-ecm and nep-for
Note: 337895
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Analysis of Variance for Bayesian Inference (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20111409

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