Methods for computing marginal data densities from the Gibbs output

Cristina Fuentes-Albero and Leonardo Melosi

Journal of Econometrics, 2013, vol. 175, issue 2, 132-141

Abstract: We introduce two estimators for estimating the Marginal Data Density (MDD) from the Gibbs output. Our methods are based on exploiting the analytical tractability condition, which requires that some parameter blocks can be analytically integrated out from the conditional posterior densities. This condition is satisfied by several widely used time series models. An empirical application to six-variate VAR models shows that the bias of a fully computational estimator is sufficiently large to distort the implied model rankings. One of the estimators is fast enough to make multiple computations of MDDs in densely parameterized models feasible.

Keywords: Marginal likelihood; Gibbs sampler; Time series econometrics; Bayesian econometrics; Reciprocal importance sampling (search for similar items in EconPapers)
JEL-codes: C11 C15 C16 C32 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Related works:
Working Paper: Methods for Computing Marginal Data Densities from the Gibbs Output (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:175:y:2013:i:2:p:132-141

DOI: 10.1016/j.jeconom.2013.03.002

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