期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2009
卷号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：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.
