New Methods for the Analysis of Long-Memory Time Series

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dc.contributor.author Delgado, Miguel A.
dc.contributor.author Robinson, Peter M.
dc.date.accessioned 2009-11-11T13:11:28Z
dc.date.available 2009-11-11T13:11:28Z
dc.date.issued 1994
dc.identifier.bibliographicCitation Journal of Forecasting. 1994, vol.13, p. 97-107
dc.identifier.issn 0277-6693
dc.identifier.uri http://hdl.handle.net/10016/2391
dc.description.abstract Some recent developments in the analysis of time series are applied to real economic data. It is assumed that any stochastic or nonstochastic trends have been removed from the raw observed time series. Models for long-memory time series are considered in which the autocovariance sequence is parameterized only at very long lags or the spectral density is parameterized only at very low frequencies. The recently proposed methods for estimating the differencing parameters are applied to an economic time series of prices in Spain. The results consistently indicate that the inflation series suffers from long memory. The corelogram and periodogram for the resulting filtered series are plotted. The autocorrelation estimates are very close to zero. However, the seasonal peaks are still present
dc.format.mimetype text/plain
dc.format.mimetype application/pdf
dc.language.iso eng
dc.language.iso eng
dc.publisher John Wiley & Sons
dc.rights © John Wiley & Sons
dc.title New Methods for the Analysis of Long-Memory Time Series
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://proquest.umi.com/pqdweb?did=586590&sid=1&Fmt=3&clientId=36295&RQT=309&VName=PQD
dc.subject.eciencia Economía
dc.rights.accessRights openAccess
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