Arbeitspapier
Methods for computing marginal data densities from the gibbs output
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.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 2011-31
- Classification
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Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
- Subject
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marginal likelihood
Gibbs sampler
time series econometrics
Bayesian econometrics
reciprocal importance sampling
- Event
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Geistige Schöpfung
- (who)
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Fuentes-Albero, Cristina
Melosi, Leonardo
- Event
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Veröffentlichung
- (who)
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Rutgers University, Department of Economics
- (where)
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New Brunswick, NJ
- (when)
-
2011
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Fuentes-Albero, Cristina
- Melosi, Leonardo
- Rutgers University, Department of Economics
Time of origin
- 2011
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