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dc.contributor.authorDeo, Rohit S.-
dc.contributor.authorHurvich, Clifford M.-
dc.date.accessioned2006-06-22T13:42:17Z-
dc.date.available2006-06-22T13:42:17Z-
dc.date.issued1998-
dc.identifier.urihttp://hdl.handle.net/2451/14777-
dc.description.abstractWe consider semiparametric estimation of the memory parameter in a long memory stochastic volatility model. We study the estimator based on a log periodogram regression as originally proposed by Geweke and Porter-Hudak (1983, Journal of Time Series Analysis 4, 221–238). Expressions for the asymptotic bias and variance of the estimator are obtained, and the asymptotic distribution is shown to be the same as that obtained in recent literature for a Gaussian long memory series. The theoretical result does not require omission of a block of frequencies near the origin. We show that this ability to use the lowest frequencies is particularly desirable in the context of the long memory stochastic volatility model.en
dc.format.extent159386 bytes-
dc.format.mimetypeapplication/pdf-
dc.languageEnglishEN
dc.language.isoen
dc.publisherStern School of Business, New York Universityen
dc.relation.ispartofseriesSOR-98-4en
dc.titleON THE LOG PERIODOGRAM REGRESSION ESTIMATOR OF THE MEMORY PARAMETER IN LONG MEMORY STOCHASTIC VOLATILITY MODELSen
dc.typeWorking Paperen
dc.description.seriesStatistics Working Papers SeriesEN
Appears in Collections:IOMS: Statistics Working Papers

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