Empirical prediction intervals revisited

Cited 29 time in webofscience Cited 25 time in scopus
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dc.contributor.authorLee, Yun Shinko
dc.contributor.authorScholtes, Stefanko
dc.date.accessioned2014-08-27-
dc.date.available2014-08-27-
dc.date.created2014-01-09-
dc.date.created2014-01-09-
dc.date.issued2014-04-
dc.identifier.citationINTERNATIONAL JOURNAL OF FORECASTING, v.30, no.2, pp.217 - 234-
dc.identifier.issn0169-2070-
dc.identifier.urihttp://hdl.handle.net/10203/187256-
dc.description.abstractEmpirical prediction intervals are constructed based on the distribution of previous out-of-sample forecast errors. Given historical data, a sample of such forecast errors is generated by successively applying a chosen point forecasting model to a sequence of fixed windows of past observations and recording the associated deviations of the model predictions from the actual observations out-of-sample. The suitable quantiles of the distribution of these forecast errors are then used along with the point forecast made by the selected model to construct an empirical prediction interval. This paper re-examines the properties of the empirical prediction interval. Specifically, we provide conditions for its asymptotic validity, evaluate its small sample performance and discuss its limitations. (C) 2013 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.subjectTIME-SERIES-
dc.subjectQUANTILE REGRESSION-
dc.subjectFORECASTS-
dc.subjectUNCERTAINTY-
dc.subjectMODELS-
dc.subjectTESTS-
dc.titleEmpirical prediction intervals revisited-
dc.typeArticle-
dc.identifier.wosid000334089700004-
dc.identifier.scopusid2-s2.0-84890055835-
dc.type.rimsART-
dc.citation.volume30-
dc.citation.issue2-
dc.citation.beginningpage217-
dc.citation.endingpage234-
dc.citation.publicationnameINTERNATIONAL JOURNAL OF FORECASTING-
dc.identifier.doi10.1016/j.ijforecast.2013.07.018-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorLee, Yun Shin-
dc.contributor.nonIdAuthorScholtes, Stefan-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorInterval forecasting-
dc.subject.keywordAuthorProbabilistic forecasting-
dc.subject.keywordAuthorOut-of-sample forecast error-
dc.subject.keywordAuthorModel uncertainty-
dc.subject.keywordAuthorNon-Gaussian distribution-
dc.subject.keywordPlusTIME-SERIES-
dc.subject.keywordPlusQUANTILE REGRESSION-
dc.subject.keywordPlusFORECASTS-
dc.subject.keywordPlusUNCERTAINTY-
dc.subject.keywordPlusMODELS-
dc.subject.keywordPlusTESTS-
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