Computational Statistics & Data Analysis
Maximizing equity market sector predictability in a Bayesian time-varying parameter model
Computational Statistics & Data Analysis
Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling
Computational Statistics & Data Analysis
Leverage, heavy-tails and correlated jumps in stochastic volatility models
Computational Statistics & Data Analysis
The marginal likelihood of dynamic mixture models
Computational Statistics & Data Analysis
A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood
Computational Statistics & Data Analysis
Change point models for cognitive tests using semi-parametric maximum likelihood
Computational Statistics & Data Analysis
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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 one to select a value in the parameter space at which the computation is performed. Bayesian inference for a change-point dynamic regression model and the computation of its marginal likelihood are explained. Motivated by 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. However, taking into account the precision of the marginal likelihood estimator, the overall recommendation is to use the posterior mode or median. 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.