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| Title: | | Methods for computing marginal data densities from the gibbs output  |
| 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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