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Title:Methods for computing marginal data densities from the gibbs output PDF Logo
Authors:Fuentes-Albero, Cristina
Melosi, Leonardo
Issue Date:2011
Series/Report no.:Working Papers, Department of Economics, Rutgers, the State University of New Jersey 2011,31
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.
Subjects:marginal likelihood
Gibbs sampler
time series econometrics
Bayesian econometrics
reciprocal importance sampling
JEL:C11
C15
C16
Document Type:Working Paper
Appears in Collections:Working Papers, Department of Economics, Rutgers University

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