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Title:Forecasting long memory time series under a break in persistence PDF Logo
Authors:Heinen, Florian
Sibbertsen, Philipp
Kruse, Robinson
Issue Date:2009
Series/Report no.:Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät // Wirtschaftswissenschaftliche Fakultät, Leibniz Universität Hannover 433
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.
JEL:C15
C22
C53
Document Type:Working Paper
Appears in Collections:Diskussionspapiere, Wirtschaftswissenschaftliche Fakultät, Universität Hannover

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