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

Yong Song ()

Working Papers from University of Toronto, Department of Economics

Abstract: This paper proposes an infinite dimension Markov switching model to accommodate regime switching and structural break dynamics or a combination of both in a 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 nonparametric approach allows for regime persistence and estimates the number of states automatically. A global identification algorithm for structural changes versus regime switching is presented. Applications to U.S. real interest rates and inflation compare the new model to existing parametric alternatives. Besides identifying episodes of regime switching and structural breaks, the hierarchical distribution governing the parameters of the conditional data density provides significant gains to forecasting precision.

Keywords: hidden Markov model; Bayesian nonparametrics; Dirchlet process (search for similar items in EconPapers)
JEL-codes: C11 C22 C51 C53 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2011-04-15
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-427

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