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Forecasting time series with long memory and level shifts

Author

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  • Philip Hans Franses

    (Erasmus University Rotterdam, The Netherlands)

  • Namwon Hyung

    (University of Seoul, Korea)

Abstract

It is well known that some economic time series can be described by models which allow for either long memory or for occasional level shifts. In this paper we propose to examine the relative merits of these models by introducing a new model, which jointly captures the two features. We discuss representation and estimation. Using simulations, we demonstrate its forecasting ability, relative to the one-feature models, both in terms of point forecasts and interval forecasts. We illustrate the model for daily S&P500 volatility. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Philip Hans Franses & Namwon Hyung, 2005. "Forecasting time series with long memory and level shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 1-16.
    • Handle: RePEc:jof:jforec:v:24:y:2005:i:1:p:1-16
    DOI: 10.1002/for.937
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    References listed on IDEAS

    as
    1. Philip Hans Franses & Marius Ooms & Charles S. Bos, 1999. "Long memory and level shifts: Re-analyzing inflation rates," Empirical Economics, Springer, vol. 24(3), pages 427-449.
    2. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    3. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    4. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    5. Engle, Robert F & Smith, Aaron, 1998. "Stochastic Permanent Breaks," University of California at San Diego, Economics Working Paper Series qt99v0s0zx, Department of Economics, UC San Diego.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Robert F. Engle & Aaron D. Smith, 1999. "Stochastic Permanent Breaks," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 553-574, November.
    8. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
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    Cited by:

    1. Terence C. Mills, 2007. "Time series modelling of two millennia of northern hemisphere temperatures: long memory or shifting trends?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 83-94, January.
    2. Dominique Guegan, 2007. "Global and local stationary modelling in finance: theory and empirical evidence," Post-Print halshs-00187875, HAL.
    3. Dominique Guegan, 2008. "Non-stationarity and meta-distribution," Post-Print halshs-00270708, HAL.
    4. Franses, Philip Hans & Janssens, Eva, 2018. "Inflation in Africa, 1960–2015," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 57(C), pages 261-292.
    5. Dominique Guegan & Zhiping Lu, 2007. "A note on self-similarity for discrete time series," Post-Print halshs-00187910, HAL.

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