Modelling Regime Switching and Structural Breaks with an Infinite Hidden Markov Model

This paper proposes an infinite hidden Markov model to integrate the regime switching and the structural break dynamics in a single, coherent Bayesian framework. Two parallel hierarchical structures, one governing the transition probabilities and another governing the parameters of the conditional data density, keep the model parsimonious and improve forecasts. This flexible approach allows for regime persistence and estimates the number of states automatically. A global identification methodology for structural changes versus regime switching is presented. An application to U.S. real interest rates compares the new model to existing parametric alternatives.