Forecasting long memory time series under a break in persistence
Florian Heinen (),
Philipp Sibbertsen () and
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Florian Heinen: Leibniz University of Hannover, Postal: Institute of Statistics, Faculty of Economics and Management, Leibniz University of Hannover, D-30167 Hannover, Germany
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
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.
Keywords: Long memory time series; Break in persistence; Structural change; Simulation; Forecasting competition (search for similar items in EconPapers)
JEL-codes: C15 C22 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Working Paper: Forecasting long memory time series under a break in persistence (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2009-53
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