Non-stationary log-periodogram regression

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dc.contributor.author Velasco, Carlos
dc.date.accessioned 2009-06-04T11:15:42Z
dc.date.available 2009-06-04T11:15:42Z
dc.date.issued 1999-08
dc.identifier.bibliographicCitation Journal of Econometrics. 1999, vol. 91, nº 2, p. 325-371
dc.identifier.issn 0304-4076
dc.identifier.uri http://hdl.handle.net/10016/4346
dc.description.abstract We study asymptotic properties of the log-periodogram semiparametric estimate of the memory parameter d for non-stationary (d>=1/2) time series with Gaussian increments, extending the results of Robinson (1995) for stationary and invertible Gaussian processes. We generalize the definition of the memory parameter d for non-stationary processes in terms of the (successively) differentiated series. We obtain that the log-periodogram estimate is asymptotically normal for dE[1/2, 3/4) and still consistent for dE[1/2, 1). We show that with adequate data tapers, a modified estimate is consistent and asymptotically normal distributed for any d, including both non-stationary and non-invertible processes. The estimates are invariant to the presence of certain deterministic trends, without any need of estimation.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.rights © Elsevier
dc.subject.other Non-stationary time series
dc.subject.other Log-periodogram regression
dc.subject.other Semiparametric inference
dc.subject.other Tapering
dc.title Non-stationary log-periodogram regression
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1016/S0304-4076(98)00080-3
dc.subject.eciencia Economía
dc.identifier.doi 10.1016/S0304-4076(98)00080-3
dc.rights.accessRights openAccess
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