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Timing structural change: a conditional probabilistic approach

Author

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  • David N. DeJong

    (Department of Economics, University of Pittsburgh, USA)

  • Roman Liesenfeld

    (Department of Economics, Universität Kiel, Germany)

  • Jean-Francois Richard

    (Department of Economics, University of Pittsburgh, USA)

Abstract

We propose a strategy for assessing structural stability in time-series frameworks when potential change dates are unknown. Existing stability tests are effective in detecting structural change, but procedures for identifying timing are imprecise, especially in assessing the stability of variance parameters. We present a likelihood-based procedure for assigning conditional probabilities to the occurrence of structural breaks at alternative dates. The procedure is effective in improving the precision with which inferences regarding timing can be made. We illustrate parametric and non-parametric implementations of the procedure through Monte Carlo experiments, and an assessment of the volatility reduction in the growth rate of US GDP. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • David N. DeJong & Roman Liesenfeld & Jean-Francois Richard, 2006. "Timing structural change: a conditional probabilistic approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 175-190.
    • Handle: RePEc:jae:japmet:v:21:y:2006:i:2:p:175-190
    DOI: 10.1002/jae.821
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    References listed on IDEAS

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    1. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
    2. 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.
    3. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed and Why?," NBER Chapters, in: NBER Macroeconomics Annual 2002, Volume 17, pages 159-230, National Bureau of Economic Research, Inc.
    4. 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 374-386, July.
    5. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    6. Hendry, David F., 1984. "Monte carlo experimentation in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 16, pages 937-976, Elsevier.
    7. 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.
    8. Gabriel Perez-Quiros & Margaret M. McConnell, 2000. "Output Fluctuations in the United States: What Has Changed since the Early 1980's?," American Economic Review, American Economic Association, vol. 90(5), pages 1464-1476, December.
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    Cited by:

    1. Dave, Chetan & Dressler, Scott, 2007. "Market structure and business cycles: Do nominal rigidities influence the importance of real shocks?," MPRA Paper 1794, University Library of Munich, Germany.
    2. Christian Aßmann & Jens Hogrefe & Roman Liesenfeld, 2009. "The decline in German output volatility: a Bayesian analysis," Empirical Economics, Springer, vol. 37(3), pages 653-679, December.

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