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Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models

  • Giordani, Paolo


    (Research Department, Central Bank of Sweden)

  • Kohn, Robert

    (School of Economics, School of Banking and Finance)

Time series subject to parameter shifts of random magnitude and timing are commonly modeled with a change-point approach using Chib's (1998) algorithm to draw the break dates. We outline some advantages of an alternative approach in which breaks come through mixture distributions in state innovations, and for which the sampler of Gerlach, Carter and Kohn (2000) allows reliable and efficient inference. We show how this approach can be used to (i) model shifts in variance that occur independently of shifts in other parameters (ii) draw the break dates efficiently in change-point and regime-switching models with either Markov or non-Markov transition probabilities. We extend the proofs given in Carter and Kohn (1994) and in Gerlach, Carter and Kohn (2000) to state-space models with system matrices which are functions of lags of the dependent variables, and we further improve the algorithms in Gerlach, Carter and Kohn by introducing to the time series literature the concept of adaptive Metropolis-Hastings sampling for discrete latent variable models. We develop an easily implemented adative algorithm that promises to sizably reduce computing time in a variety of problems including mixture innovation, change-point, regime-switching, and outlier detection.

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Paper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 196.

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Length: 39 pages
Date of creation: 01 May 2006
Date of revision:
Handle: RePEc:hhs:rbnkwp:0196
Contact details of provider: Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden
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  1. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
  2. George A. Akerlof & William T. Dickens & George L. Perry, 2000. "Near-Rational Wage and Price Setting and the Long-Run Phillips Curve," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 31(1), pages 1-60.
  3. Giordani, Paolo & Soderlind, Paul, 2000. "Inflation Forecast Uncertainty," SSE/EFI Working Paper Series in Economics and Finance 384, Stockholm School of Economics, revised 09 Oct 2000.
  4. David J. Nott & Robert Kohn, 2005. "Adaptive sampling for Bayesian variable selection," Biometrika, Biometrika Trust, vol. 92(4), pages 747-763, December.
  5. Sangjoon Kim, Neil Shephard & Siddhartha Chib, . "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
  6. Carter, C.K. & Kohn, R., . "Semiparametric Bayesian inference for time series with mixed spectra," Statistics Working Paper _005, Australian Graduate School of Management.
  7. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Universite de Montreal, Departement de sciences economiques.
  8. Gary M. Koop & Simon M. Potter, 2004. "Forecasting and estimating multiple change-point models with an unknown number of change points," Staff Reports 196, Federal Reserve Bank of New York.
  9. Gary Koop & Simon M. Potter, 2009. "Prior Elicitation In Multiple Change-Point Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 751-772, 08.
  10. Timothy Cogley & Thomas Sargent, . "Evolving Post-World War II U.S. Inflation Dynamics," Working Papers 2132872, Department of Economics, W. P. Carey School of Business, Arizona State University.
  11. Christopher A. Sims, 1993. "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 179-212 National Bureau of Economic Research, Inc.
  12. James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
  13. Giordani, P. & Kohn, R. & van Dijk, D.J.C., 2005. "A unified approach to nonlinearity, structural change and outliers," Econometric Institute Research Papers EI 2005-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  14. Bruce E. Hansen, 2001. "The New Econometrics of Structural Change: Dating Breaks in U.S. Labour Productivity," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 117-128, Fall.
  15. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, June.
  16. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
  17. Pesaran, M Hashem & Pettenuzzo, Davide & Timmermann, Allan G, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," CEPR Discussion Papers 4636, C.E.P.R. Discussion Papers.
  18. Thomas Sargent & Noah Williams & Tao Zha, 2006. "The Conquest of South American Inflation," NBER Working Papers 12606, National Bureau of Economic Research, Inc.
  19. Schorfheide, Frank, 2000. "Forecasting Economic Time Series," Econometric Theory, Cambridge University Press, vol. 16(03), pages 441-450, June.
  20. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "Inflation and monetary policy in the twentieth century," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q I, pages 22-45.
  21. J Huston McCulloch, 2000. "State-Space Times Series Modeling of Structural Breaks," Working Papers 00-11, Ohio State University, Department of Economics.
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