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Forecasting long memory time series under a break in persistence

Author

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  • Heinen, Florian
  • Sibbertsen, Philipp
  • Kruse, Robinson
Abstract
We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of this effect depends on whether the memory parameter is increasing or decreasing over time. A comparison of six forecasting strategies allows us to conclude that pre-testing for a change in persistence is highly recommendable in our setting. In addition we provide an empirical example which underlines the importance of our findings.

Suggested Citation

  • Heinen, Florian & Sibbertsen, Philipp & Kruse, Robinson, 2009. "Forecasting long memory time series under a break in persistence," Hannover Economic Papers (HEP) dp-433, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-433
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    References listed on IDEAS

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    Cited by:

    1. Michael Frömmel & Robinson Kruse, 2012. "Testing for a rational bubble under long memory," Quantitative Finance, Taylor & Francis Journals, vol. 12(11), pages 1723-1732, November.
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    3. Sibbertsen, Philipp & Wegener, Christoph & Basse, Tobias, 2014. "Testing for a break in the persistence in yield spreads of EMU government bonds," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 109-118.
    4. Heinen, Florian & Willert, Juliane, 2011. "Monitoring a change in persistence of a long range dependent time series," Hannover Economic Papers (HEP) dp-479, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

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    More about this item

    Keywords

    Long memory time series; Break in persistence; Structural change; Simulation; Forecasting competition;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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