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Learning, Forecasting and Structural Breaks

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  • John M. Maheu
  • Stephen Gordon

Abstract

The literature on structural breaks focuses on ex post identification of break points that may have occurred in the past. While this question is important, a more challenging problem facing econometricians is to provide forecasts when the data generating process is unstable. The purpose of this paper is to provide a general methodology for forecasting in the presence of model instability. We make no assumptions on the number of break points or the law of motion governing parameter changes. Our approach makes use of Bayesian methods of model comparison and learning in order to provide an optimal predictive density from which forecasts can be derived. Estimates for the posterior probability that a break occurred at a particular point in the sample are generated as a byproduct of our procedure. We discuss the importance of using priors that accurately reflect the econometrician's opinions as to what constitutes a plausible forecast. Several applications to macroeconomic time-series data demonstrate the usefulness of our procedure.

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Bibliographic Info

Paper provided by CIRPEE in its series Cahiers de recherche with number 0422.

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Date of creation: 2004
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Handle: RePEc:lvl:lacicr:0422

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Keywords: Bayesian Model Averaging; Markov Chain Monte Carlo; Real GDP Growth; Phillip's Curve;

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  1. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 26, pages 66-77, January.
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  3. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," CESifo Working Paper Series 1237, CESifo Group Munich.
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  7. Andrews, Donald W. K. & Lee, Inpyo & Ploberger, Werner, 1996. "Optimal changepoint tests for normal linear regression," Journal of Econometrics, Elsevier, Elsevier, vol. 70(1), pages 9-38, January.
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  9. 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.
  10. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, Elsevier, vol. 86(2), pages 221-241, June.
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  19. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(2), pages 147-62, April.
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  21. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics, Elsevier, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649 Elsevier.
  22. John F. Geweke, 1994. "Bayesian comparison of econometric models," Working Papers 532, Federal Reserve Bank of Minneapolis.
  23. Gary Koop & Simon M. Potter, 1999. "Are apparent findings of nonlinearity due to structural instability in economic time series?," Staff Reports 59, Federal Reserve Bank of New York.
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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Economic growth and convergence
    by Stephen Gordon in Worthwhile Canadian Initiative on 2009-12-24 11:00:00
  2. Economic growth and convergence
    by Stephen in Worthwhile Canadian Initiative on 2006-03-26 01:24:17
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