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http://hdl.handle.net/10419/37129
  

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dc.contributor.authorHeinen, Florianen_US
dc.contributor.authorSibbertsen, Philippen_US
dc.contributor.authorKruse, Robinsonen_US
dc.date.accessioned2009-12-01en_US
dc.date.accessioned2010-08-03T13:10:52Z-
dc.date.available2010-08-03T13:10:52Z-
dc.date.issued2009en_US
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_US
dc.language.isoengen_US
dc.publisherWirtschaftswiss. Fak., Leibniz Univ. Hannoveren_US
dc.relation.ispartofseriesDiskussionspapiere der Wirtschaftswissenschaftlichen Fakultät // Wirtschaftswissenschaftliche Fakultät, Leibniz Universität Hannover 433en_US
dc.subject.jelC15en_US
dc.subject.jelC22en_US
dc.subject.jelC53en_US
dc.subject.ddc330en_US
dc.subject.stwZeitreihenanalyseen_US
dc.subject.stwStrukturbruchen_US
dc.subject.stwSimulationen_US
dc.subject.stwPrognoseverfahrenen_US
dc.titleForecasting long memory time series under a break in persistenceen_US
dc.typeWorking Paperen_US
dc.identifier.ppn613225317en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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