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dc.contributor.authorDavidson, Jamesen_GB
dc.contributor.authorHashimzade, Nigaren_GB
dc.contributor.departmentUniversity of Exeter; Hashimzade now at University of Readingen_GB
dc.date.accessioned2009-01-27T16:46:56Zen_GB
dc.date.accessioned2011-01-25T10:25:36Zen_GB
dc.date.accessioned2013-03-19T15:56:16Z
dc.date.issued2009-01-27T16:46:56Zen_GB
dc.description.abstractThe so-called type I and type II fractional Brownian motions are limit distributions associated with the fractional integration model in which pre-sample shocks are either included in the lag structure, or suppressed. There can be substantial differences between the distributions of these two processes and of functionals derived from them, so that it becomes an important issue to decide which model to use as a basis for inference. Alternative methods for simulating the type I case are contrasted, and for models close to the nonstationarity boundary, truncating infinite sums is shown to result in a significant distortion of the distribution. A simple simulation method that overcomes this problem is described and implemented. The approach also has implications for the estimation of type I ARFIMA models, and a new conditional ML estimator is proposed, using the annual Nile minima series for illustration.en_GB
dc.identifier.doidoi:10.1016/j.csda.2008.11.008en_GB
dc.identifier.urihttp://hdl.handle.net/10036/48077en_GB
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.relation.ispartofseriesDiscussion Papers in Economicsen_GB
dc.relation.ispartofseries08/16en_GB
dc.relation.urlhttp://www.sciencedirect.com/science/journal/01679473en_GB
dc.subjectfractional Brownian motionsen_GB
dc.subjectlong memoryen_GB
dc.subjectsimulationen_GB
dc.subjectARFIMAen_GB
dc.titleType I and type II fractional Brownian motions: a reconsiderationen_GB
dc.typeArticleen_GB
dc.typeWorking Paperen_GB
dc.date.available2009-01-27T16:46:56Zen_GB
dc.date.available2011-01-25T10:25:36Zen_GB
dc.date.available2013-03-19T15:56:16Z
dc.descriptionAuthors' draft published as Discussion paperen_GB
dc.identifier.journalComputational Statistics & Data Analysisen_GB


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