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On marginal likelihood computation in change-point models

Luc Bauwens () and Jeroen Rombouts ()

No 2009061, CORE Discussion Papers from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: Change-point models are useful for modeling time 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.

Keywords: BIC; change-point model; Chib's method; marginal likelihood (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-for
Date: 2009-10-01
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Related works:
Journal Article: On marginal likelihood computation in change-point models (2012) Downloads
Working Paper: On marginal likelihood computation in change-point models (2012) Downloads
Working Paper: On Marginal Likelihood Computation in Change-point Models (2009) Downloads
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