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dc.contributor.authorKapetanios, Georgeen_US
dc.contributor.authorPsaradakis, Zachariasen_US
dc.date.accessioned2012-09-20T13:00:53Z-
dc.date.available2012-09-20T13:00:53Z-
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/10419/62875-
dc.description.abstractThis paper studies the properties of the sieve bootstrap for a class of linear processes which exhibit strong dependence. The sieve bootstrap scheme is based on residual resampling from autoregressive approximations the order of which increases slowly with the sample size. The first-order asymptotic validity of the sieve bootstrap is established in the case of the sample mean and sample autocovariances. The finite-sample properties of the method are also investigated by means of Monte Carlo experiments.en_US
dc.language.isoengen_US
dc.publisherQueen Mary, Univ. of London, Dep. of Economics Londonen_US
dc.relation.ispartofseriesWorking Paper, Department of Economics, Queen Mary, University of London 552en_US
dc.subject.jelC10en_US
dc.subject.jelC22en_US
dc.subject.jelC50en_US
dc.subject.ddc330en_US
dc.subject.keywordAutoregressive approximation, Linear process, Strong dependence, Sieve bootstrap, Stationary processen_US
dc.titleSieve bootstrap for strongly dependent stationary processesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn506651193en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:Working Paper Series, School of Economics and Finance, Queen Mary, University of London

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