A bayesian analysis of some threshold switching models

Abstract

This paper presents a Bayesian analysis of various threshold switching regression models, including simple time series models, where the change of regime is governed by a known function of exogenous variables. Some special features arising from the choice of a two-dimensional linear dichotomy function are then discussed and the concepts of concurrency and duality introduced. Within this framework, we compare the maximum likelihood and Bayesian methodologies for inference and prediction. In particular, we show that the Bayesian approach solves the non-uniqueness problem which affects maximum likelihood prediction in certain situations. These results are illustrated with two numerical examples.