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Forecasting long memory time series under a break in persistence

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Author Info

  • Florian Heinen

    ()
    (Leibniz University of Hannover)

  • Philipp Sibbertsen

    (Leibniz University of Hannover)

  • Robinson Kruse

    ()
    (Aarhus University and CREATES)

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.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2009-53.

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Length: 29
Date of creation: 17 Nov 2009
Date of revision:
Handle: RePEc:aah:create:2009-53

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Long memory time series; Break in persistence; Structural change; Simulation; Forecasting competition;

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References

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Citations

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Cited by:
  1. Michael Fr�mmel & Robinson Kruse, 2012. "Testing for a rational bubble under long memory," Quantitative Finance, Taylor & Francis Journals, vol. 12(11), pages 1723-1732, November.
  2. Sibbertsen, Philipp & Wegener, Christoph & Basse, Tobias, 2013. "Testing for a Break in the Persistence in Yield Spreads of EMU Government Bonds," Hannover Economic Papers (HEP) dp-517, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  3. 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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