Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/37129 
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dc.contributor.authorHeinen, Florianen
dc.contributor.authorSibbertsen, Philippen
dc.contributor.authorKruse, Robinsonen
dc.date.accessioned2009-12-01-
dc.date.accessioned2010-08-03T13:10:52Z-
dc.date.available2010-08-03T13:10:52Z-
dc.date.issued2009-
dc.identifier.urihttp://hdl.handle.net/10419/37129-
dc.description.abstractWe 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.en
dc.language.isoengen
dc.publisher|aLeibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät |cHannoveren
dc.relation.ispartofseries|aDiskussionsbeitrag |x433en
dc.subject.jelC15en
dc.subject.jelC22en
dc.subject.jelC53en
dc.subject.ddc330en
dc.subject.stwZeitreihenanalyseen
dc.subject.stwStrukturbruchen
dc.subject.stwSimulationen
dc.subject.stwPrognoseverfahrenen
dc.titleForecasting long memory time series under a break in persistence-
dc.type|aWorking Paperen
dc.identifier.ppn613225317en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:han:dpaper:dp-433en

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