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Forecasting long memory time series under a break in persistence

  • Heinen, Florian
  • Sibbertsen, Philipp
  • Kruse, Robinson

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 Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Hannover Economic Papers (HEP) with number dp-433.

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Length: 29 pages
Date of creation: Nov 2009
Date of revision:
Handle: RePEc:han:dpaper:dp-433
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