The approximation of long-memory processes by an ARMA model

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dc.contributor.authorBasak, GK
dc.contributor.authorChan, NH
dc.contributor.authorPalma, W
dc.date.accessioned2025-01-21T01:30:43Z
dc.date.available2025-01-21T01:30:43Z
dc.date.issued2001
dc.description.abstractA mean square error criterion is proposed in this paper to provide a systematic approach to approximate a long-memory time series by a short-memory ARMA(1, 1) process. Analytic expressions are derived to assess the effect of such an approximation. These results are established not only for the pure fractional noise case, but also for a general autoregressive fractional moving average long-memory time series. Performances of the ARMA(1,1) approximation as compared to using an ARFIMA model are illustrated by both computations and an application to the Nile river series. Results derived in this paper shed light on the forecasting issue of a long-memory process. Copyright (C) 2001 John Wiley & Sons, Ltd.
dc.fuente.origenWOS
dc.identifier.issn0277-6693
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/96870
dc.identifier.wosidWOS:000171229500001
dc.issue.numero6
dc.language.isoen
dc.pagina.final389
dc.pagina.inicio367
dc.revistaJournal of forecasting
dc.rightsacceso restringido
dc.subjectARMA(1,1)
dc.subjectforecast error
dc.subjectlong memory
dc.subject.ods08 Decent Work and Economic Growth
dc.subject.odspa08 Trabajo decente y crecimiento económico
dc.titleThe approximation of long-memory processes by an ARMA model
dc.typeartículo
dc.volumen20
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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