State space modeling of long-memory processes

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dc.contributor.authorChan, NH
dc.contributor.authorPalma, W
dc.date.accessioned2024-01-10T12:43:00Z
dc.date.available2024-01-10T12:43:00Z
dc.date.issued1998
dc.description.abstractThis paper develops a state space modeling for long-range dependent data. Although a long-range dependent process has an infinite-dimensional state space representation, it is shown that by using the Kalman filter, the exact likelihood function can be computed recursively in a finite number of steps. Furthermore, an approximation to the likelihood function based on the truncated state space equation is considered. Asymptotic properties of these approximate maximum likelihood estimates are established for a class of long-range dependent models, namely, the fractional autoregressive moving average models. Simulation studies show rapid converging properties of the approximate maximum likelihood approach.
dc.fechaingreso.objetodigital2024-07-09
dc.format.extent22 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1214/aos/1028144856
dc.identifier.issn0090-5364
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/77559
dc.identifier.wosidWOS:000079135400013
dc.information.autorucMatemática;Palma W;S/I;100091
dc.issue.numero2
dc.language.isoen
dc.nota.accesoContenido completo
dc.pagina.final740
dc.pagina.inicio719
dc.publisherINST MATHEMATICAL STATISTICS
dc.revistaANNALS OF STATISTICS
dc.rightsacceso abierto
dc.subjectARFIMA
dc.subjectasymptotic normality
dc.subjectconsistency
dc.subjectefficiency
dc.subjectlong-memory
dc.subjectMLE
dc.subjecttruncated state space
dc.subjectTIME-SERIES MODELS
dc.subject.ods08 Decent Work and Economic Growth
dc.subject.odspa08 Trabajo decente y crecimiento económico
dc.titleState space modeling of long-memory processes
dc.typeartículo
dc.volumen26
sipa.codpersvinculados100091
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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