On Marginal Likelihood Computation in Change-point Models

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On Marginal Likelihood Computation in Change-point Models

Author Info

Luc Bauwens
Jeroen V.K. Rombouts

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Abstract

Change-point models are useful for modeling times series subject to structural breaks. For interpretation and forecasting, it is essential to estimate correctly the number of change points in this class of models. In Bayesian inference, the number of change-points is typically chosen by the marginal likelihood criterion, computed by Chib’s method. This method requires to select a value in the parameter space at which the computation is done. We explain in detail how to perform Bayesian inference for a change point dynamic regression model and how to compute its marginal likelihood. Motivated by our results from three empirical illustrations, a simulation study shows that Chib’s method is robust with respect to the choice of the parameter value used in the computations, among posterior mean, mode and quartiles. Furthermore, the performance of the Bayesian information criterion, which is based on maximum likelihood estimates, in selecting the correct model is comparable to that of the marginal likelihood.

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Paper provided by CIRPEE in its series Cahiers de recherche with number 0942.

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Date of creation: 2009
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Handle: RePEc:lvl:lacicr:0942

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Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

This paper has been announced in the following NEP Reports:

NEP-ALL-2009-10-24 (All new papers)
NEP-ECM-2009-10-24 (Econometrics)

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