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

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  • Yong Song

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.

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File URL: http://www.economics.utoronto.ca/public/workingPapers/tecipa-427.pdf
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Bibliographic Info

Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-427.

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Length: 49 pages
Date of creation: 15 Apr 2011
Date of revision:
Handle: RePEc:tor:tecipa:tecipa-427

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Related research

Keywords: hidden Markov model; Bayesian nonparametrics; Dirchlet process;

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Cited by:
  1. DUFAYS, Arnaud, 2012. "Infinite-state Markov-switching for dynamic volatility and correlation models," CORE Discussion Papers 2012043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. repec:luc:wpaper:14-07 is not listed on IDEAS

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