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

  • Yong Song

    ()

    (University of Technology, Sydney, Australia; RCEA, Italy)

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.

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File URL: http://www.rcfea.org/RePEc/pdf/wp28_12.pdf
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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 28_12.

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Date of creation: Jun 2012
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Handle: RePEc:rim:rimwps:28_12
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  1. Markus Jochmann, 2010. "Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach," Working Papers 1001, University of Strathclyde Business School, Department of Economics.
  2. Rose, Andrew Kenan, 1988. " Is the Real Interest Rate Stable?," Journal of Finance, American Finance Association, vol. 43(5), pages 1095-1112, December.
  3. Andrew Ang & Geert Bekaert, 1998. "Regime Switches in Interest Rates," NBER Working Papers 6508, National Bureau of Economic Research, Inc.
  4. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," CESifo Working Paper Series 1237, CESifo Group Munich.
  5. John M. Maheu & Thomas H. McCurdy, 2007. "How useful are historical data for forecasting the long-run equity return distribution?," Working Paper Series 19-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
  6. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 553-583.
  7. Geweke, John & Amisano, Gianni, 2008. "Comparing and evaluating Bayesian predictive distributions of assets returns," Working Paper Series 0969, European Central Bank.
  8. Wang, Jiahui & Zivot, Eric, 2000. "A Bayesian Time Series Model of Multiple Structural Changes in Level, Trend, and Variance," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 374-86, July.
  9. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
  10. Griffin, J.E. & Steel, M.F.J., 2011. "Stick-breaking autoregressive processes," Journal of Econometrics, Elsevier, vol. 162(2), pages 383-396, June.
  11. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
  12. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
  13. Edoardo Otranto & Giampiero Gallo, 2002. "A Nonparametric Bayesian Approach To Detect The Number Of Regimes In Markov Switching Models," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 477-496.
  14. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  15. John M. Maheu & Thomas H. McCurdy & Yong Song, 2012. "Components of Bull and Bear Markets: Bull Corrections and Bear Rallies," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 391-403, February.
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