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dc.contributor.authorKiogou, Sebastien Dalli
dc.date.accessioned2014-03-04T19:59:36Z
dc.date.available2014-03-04T19:59:36Z
dc.date.issued2011-05
dc.identifier.otherkiogou_sebastien_d_201105_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/kiogou_sebastien_d_201105_ms
dc.identifier.urihttp://hdl.handle.net/10724/27190
dc.description.abstractWe investigated three estimators of the fractional parameter d, in long memory time series. Discussed in [4], the rst estimator is based on a regression analysis using the periodogram of the long memory series; the second estimator which is discussed in [11, 10], is based on a regression analysis using a lag-window spectral density estimator; the third estimator performs a maximum likelihood estimation (MLE) of d using the fast and accurate method of [8]. To conduct our investigation, we generated synthetic ARFIMA(p; d; q) samples where d = :25 and d = :45. Computational results showed that in general the MLE method performs better in large samples whereas the [4] proposed estimator performs better in small samples. Yet, the estimator in [11, 10] behaves better than that proposed by [4] in large samples. While the MLE has the smallest standard errors in both small and large samples, the standard errors of the [4] approach are the largest.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectFractional degree of differencing
dc.subjectFractional parameter d
dc.subjectLong memory time series models
dc.subjectSpectral density
dc.subjectlag-window
dc.subjectFast and accurate MLE
dc.titleInvestigating some estimators of the fractional degree of differencing, in long memory time series
dc.typeThesis
dc.description.degreeMS
dc.description.departmentStatistics
dc.description.majorStatistics
dc.description.advisorWilliam P. McCormick
dc.description.committeeWilliam P. McCormick
dc.description.committeeWilliam D. Lastrapes
dc.description.committeeLynne Billard


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