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A New Structural Break Model with Application to Canadian Inflation Forecasting

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  • John M. Maheu

    ()
    (Department of Economics, University of Toronto, Canada; RCEA, Italy)

  • Yong Song

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

Abstract

This paper develops an efficient approach to model and forecast time-series data with an unknown number of change-points. Using a conjugate prior and conditional on time-invariant parameters, the predictive density and the posterior distribution of the change-points have closed forms. The conjugate prior is further modeled as hierarchical to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients or both. Regime duration can be modelled as a Poisson distribution. A new efficient Markov Chain Monte Carlo sampler draws the parameters as one block from the posterior distribution. An application to Canada inflation time series shows the gains in forecasting precision that our model provides.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 27_12.

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Date of creation: Jun 2012
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Handle: RePEc:rim:rimwps:27_12

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Keywords: multiple change-points; regime duration; inflation targeting; predictive density; MCMC;

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  1. John M. Maheu & Stephen Gordon, 2004. "Learning, Forecasting and Structural Breaks," Cahiers de recherche 0422, CIRPEE.
  2. Geweke, John & Jiang, Yu, 2011. "Inference and prediction in a multiple-structural-break model," Journal of Econometrics, Elsevier, vol. 163(2), pages 172-185, August.
  3. 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.
  4. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29.
  5. Engle, Robert F, 1983. "Estimates of the Variance of U.S. Inflation Based upon the ARCH Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 15(3), pages 286-301, August.
  6. 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.
  7. Chun Liu & John M Maheu, 2007. "Are there Structural Breaks in Realized Volatility?," Working Papers tecipa-304, University of Toronto, Department of Economics.
  8. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
  9. Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, vol. 137(1), pages 112-133, March.
  10. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 763-789.
  11. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
  12. Maheu, John M. & McCurdy, Thomas H., 2009. "How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution?," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 95-112.
  13. Geweke, John & Amisano, Gianni, 2008. "Comparing and evaluating Bayesian predictive distributions of assets returns," Working Paper Series 0969, European Central Bank.
  14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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