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Forecasting long memory time series under a break in persistence

  • Florian Heinen

    ()

    (Leibniz University of Hannover)

  • Philipp Sibbertsen

    (Leibniz University of Hannover)

  • Robinson Kruse

    ()

    (Aarhus University and CREATES)

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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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
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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