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Forecasting long memory time series under a break in persistence

Florian Heinen (), Philipp Sibbertsen () and Robinson Kruse
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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

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.

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
Date: 2009-11-17
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