Abstract
Bayesian methods for variable selection and model choice have become increasingly
popular in recent years, due to advances in Markov Chain Monte Carlo (MCMC)
computational algorithms. Several methods have been proposed in literature in
the case of linear and generalized linear models. In this article, we adapt some
of the most popular algorithms to a class of nonlinear and non Gaussian time
series models, i.e., the Markov Mixture Models (MMM). We also propose the
"Metropolization" of the algorithm of Kuo and Mallick (1998), in order to tackle
variable selection efficiently, both when the complexity of the model is high, as
in MMM, and when the exogenous variables are strongly correlated. Numerical
comparisons among the competing MCMC algorithms are also presented via
simulation examples.
Year
2008
Category
Refereed journal