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Methods for computing marginal data densities from the Gibbs output

  • Fuentes-Albero, Cristina
  • Melosi, Leonardo

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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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
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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