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Methods for computing marginal data densities from the Gibbs output

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  • Fuentes-Albero, Cristina
  • Melosi, Leonardo

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 175 (2013)
Issue (Month): 2 ()
Pages: 132-141

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Handle: RePEc:eee:econom:v:175:y:2013:i:2:p:132-141

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Marginal likelihood; Gibbs sampler; Time series econometrics; Bayesian econometrics; Reciprocal importance sampling;

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Cited by:
  1. Simon Gilchrist & Egon Zakrajsek & Cristina Fuentes Albero & Dario Caldara, 2013. "On the Identification of Financial and Uncertainty Shocks," 2013 Meeting Papers 965, Society for Economic Dynamics.

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