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Prior Elicitation in Multiple Change-point Models

Author

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  • Gary Koop
  • Simon M. Potter
Abstract
This paper discusses Bayesian inference in change-point models. Existing approaches involve placing a (possibly hierarchical) prior over a known number of change-points. We show how two popular priors have some potentially undesirable properties (e.g. allocating excessive prior weight to change-points near the end of the sample) and discuss how these properties relate to imposing a fixed number of changepoints in-sample. We develop a new hierarchical approach which allows some of of change-points to occur out-of sample. We show that this prior has desirable properties and handles the case where the number of change-points is unknown. Our hierarchical approach can be shown to nest a wide variety of change-point models, from timevarying parameter models to those with few (or no) breaks. Since our prior is hierarchical, data-based learning about the parameter which controls this variety occurs.

Suggested Citation

  • Gary Koop & Simon M. Potter, 2004. "Prior Elicitation in Multiple Change-point Models," Discussion Papers in Economics 04/26, Division of Economics, School of Business, University of Leicester.
    • Handle: RePEc:lec:leecon:04/26
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    5. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 66-77, January.
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    10. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
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    12. 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.
    13. Ang, Andrew & Bekaert, Geert, 2002. "Regime Switches in Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 163-182, April.
    14. Kim, Chang-Jin & Nelson, Charles R & Piger, Jeremy, 2004. "The Less-Volatile U.S. Economy: A Bayesian Investigation of Timing, Breadth, and Potential Explanations," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 80-93, January.
    15. 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.
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    17. Gary 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.
    18. Chang-Jin Kim & Charles R. Nelson, 1999. "Has The U.S. Economy Become More Stable? A Bayesian Approach Based On A Markov-Switching Model Of The Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 608-616, November.
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    Cited by:

    1. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2007. "Learning, Structural Instability, and Present Value Calculations," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 253-288.
    2. Luintel, Kul B. & Khan, Mosahid & Leon-Gonzalez, Roberto & Li, Guangjie, 2016. "Financial development, structure and growth: New data, method and results," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 95-112.
    3. Giordani, Paolo & Villani, Mattias, 2010. "Forecasting macroeconomic time series with locally adaptive signal extraction," International Journal of Forecasting, Elsevier, vol. 26(2), pages 312-325, April.
    4. Ahelegbey, Daniel Felix & Billio, Monica & Casarin, Roberto, 2024. "Modeling Turning Points in the Global Equity Market," Econometrics and Statistics, Elsevier, vol. 30(C), pages 60-75.
    5. Jochmann, Markus & Koop, Gary & Strachan, Rodney W., 2010. "Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks," International Journal of Forecasting, Elsevier, vol. 26(2), pages 326-347, April.
    6. Gary M. Koop & Simon M. Potter, 2004. "Forecasting and Estimating Multiple Change-point Models with an Unknown Number of Change-points," Discussion Papers in Economics 04/31, Division of Economics, School of Business, University of Leicester.
    7. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 66-77, January.
    8. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    9. Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.
    10. Mehmet Balcilar & Riza Demirer & Festus V. Bekun, 2021. "Flexible Time-Varying Betas in a Novel Mixture Innovation Factor Model with Latent Threshold," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    11. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    12. Lu Shaochuan, 2023. "Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation," International Statistical Review, International Statistical Institute, vol. 91(1), pages 88-113, April.
    13. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 326-360, Summer.
    14. Hinoveanu, Laurentiu C. & Leisen, Fabrizio & Villa, Cristiano, 2019. "Bayesian loss-based approach to change point analysis," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 61-78.
    15. Petros Dellaportas & David G. T. Denison & Chris Holmes, 2007. "Flexible Threshold Models for Modelling Interest Rate Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 419-437.
    16. Guangjie Li, 2015. "A stochastic frontier model with structural breaks in efficiency and technology," Empirical Economics, Springer, vol. 49(1), pages 131-159, August.

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