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Prior Elicitation In Multiple Change-Point Models

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  • Gary Koop
  • Simon M. Potter

Abstract

This article discusses Bayesian inference in change-point models. The main existing approaches treat all change-points equally, a priori, using either a Uniform prior or an informative hierarchical prior. Both approaches assume a known number of change-points. Some undesirable properties of these approaches are discussed. We develop a new Uniform prior that allows some of the change-points to occur out of sample. This prior has desirable properties, can be interpreted as "noninformative," and treats the number of change-points as unknown. Artificial and real data exercises show how these different priors can have a substantial impact on estimation and prediction. Copyright © (2009) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Suggested Citation

  • 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, August.
    • Handle: RePEc:ier:iecrev:v:50:y:2009:i:3:p:751-772
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    12. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(3), pages 763-789.
    13. 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.
    14. 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.
    15. 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. Kobi Abayomi, 2024. "How & Why To Use Audience Segmentation to Maximize (Listener) Demand Across Digital Music Portfolio," Papers 2406.09226, arXiv.org.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Lu Shaochuan, 2023. "Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation," International Statistical Review, International Statistical Institute, vol. 91(1), pages 88-113, April.
    14. 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.
    15. 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.
    16. 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.
    17. 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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