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Methods for Computing Marginal Data Densities from the Gibbs Output

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  • Cristina Fuentes-Albero

    ()
    (Rutgers, The State University of New Jersey)

  • Leonardo Melosi

    (London Business School)

Abstract

We introduce two new methods for estimating the Marginal Data Density (MDD) from the Gibbs output, which are based on exploiting the analytical tractability condition. Such a condition requires that some parameter blocks can be analytically integrated out from the conditional posterior densities. Our estimators are applicable to densely parameterized time series models such as VARs or DFMs. 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 estimator is fast enough to make multiple computations of MDDs in densely parameterized models feasible.

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Bibliographic Info

Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201131.

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Length: 20 pages
Date of creation: 17 Oct 2011
Date of revision:
Handle: RePEc:rut:rutres:201131

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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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