Analytical results for reducing the parameter space dimension when computing the marginal likelihood are given for the broad class of dynamic mixture models. These results allow the integration of scale parameters out of the likelihood by Kalman filtering and Gaussian quadrature. The method is simple and improves the accuracy of four marginal likelihood estimators, namely, the Laplace method, the Chib estimator, reciprocal importance sampling, and bridge sampling. For some empirically relevant cases like the local level and the local linear models, the marginal likelihood can be obtained directly without any posterior sampling. Implementation details are given in some examples. Two empirical applications illustrate the gain in accuracy achieved. © 2012 Elsevier B.V. All rights reserved.

The marginal likelihood of dynamic mixture models / G. Fiorentini; C. Planas; A. Rossi. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - STAMPA. - 9, 56:(2012), pp. 2650-2662.

The marginal likelihood of dynamic mixture models

G. Fiorentini;
2012

Abstract

Analytical results for reducing the parameter space dimension when computing the marginal likelihood are given for the broad class of dynamic mixture models. These results allow the integration of scale parameters out of the likelihood by Kalman filtering and Gaussian quadrature. The method is simple and improves the accuracy of four marginal likelihood estimators, namely, the Laplace method, the Chib estimator, reciprocal importance sampling, and bridge sampling. For some empirically relevant cases like the local level and the local linear models, the marginal likelihood can be obtained directly without any posterior sampling. Implementation details are given in some examples. Two empirical applications illustrate the gain in accuracy achieved. © 2012 Elsevier B.V. All rights reserved.
2012
9, 56
2650
2662
G. Fiorentini; C. Planas; A. Rossi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/646121
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