Estimation and forecasting of long-memory processes with missing values

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dc.contributor.authorPalma, W
dc.contributor.authorChan, NH
dc.date.accessioned2024-01-10T13:12:49Z
dc.date.available2024-01-10T13:12:49Z
dc.date.issued1997
dc.description.abstractThis paper addresses the issues of maximum likelihood estimation and forecasting of a long-memory time series with missing values. A state-space representation of the underlying long-memory process is proposed. By incorporating this representation with the Kalman filter, the proposed method allows not only fbr an efficient estimation of an ARFIMA model but also for the estimation of future values under the presence of missing data. This procedure is illustrated through an analysis of a foreign exchange data set. An investment scheme is developed which demonstrates the usefulness of the proposed approach. (C) 1997 John Wiley & Sons, Ltd.
dc.fechaingreso.objetodigital03-04-2024
dc.format.extent16 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1002/(SICI)1099-131X(199711)16:6<395
dc.identifier.issn0277-6693
dc.identifier.urihttps://doi.org/10.1002/(SICI)1099-131X(199711)16:6<395
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/78234
dc.identifier.wosidWOS:A1997YE88100001
dc.information.autorucMatemática;Palma W;S/I;100091
dc.issue.numero6
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final410
dc.pagina.inicio395
dc.publisherJOHN WILEY & SONS LTD
dc.revistaJOURNAL OF FORECASTING
dc.rightsacceso restringido
dc.subjectlong memory
dc.subjectARFIMA models
dc.subjectforecasting
dc.subjectmaximum likelihood estimation
dc.subjectmissing values
dc.subjectforeign exchange data
dc.subjectTIME-SERIES
dc.subjectMODELS
dc.subject.ods08 Decent Work and Economic Growth
dc.subject.odspa08 Trabajo decente y crecimiento económico
dc.titleEstimation and forecasting of long-memory processes with missing values
dc.typeartículo
dc.volumen16
sipa.codpersvinculados100091
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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