Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/153842
Authors: 
Geweke, John
Amisano, Gianni
Year of Publication: 
2011
Series/Report no.: 
ECB Working Paper 1409
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.
Subjects: 
Analysis of variance
Bayesian inference
posterior simulation
predictive distributions
JEL: 
C11
C53
Document Type: 
Working Paper

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