Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/46472
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dc.contributor.authorGao, J.en
dc.contributor.authorTong, H.en
dc.date.issued2004en
dc.identifier.citationJournal of the Royal Statistical Society Series B-Statistical Methodology, 2004; 66(2):321-336en
dc.identifier.issn1369-7412en
dc.identifier.issn0035-9246en
dc.identifier.urihttp://hdl.handle.net/2440/46472-
dc.description© 2004 Royal Statistical Societyen
dc.description.abstractSemiparametric time series regression is often used without checking its suitability, resulting in an unnecessarily complicated model. In practice, one may encounter computational difficulties caused by the curse of dimensionality.The paper suggests that to provide more precise predictions we need to choose the most significant regressors for both the parametric and the nonparametric time series components.We develop a novel cross-validation-based model selection procedure for the simultaneous choice of both the parametric and the nonparametric time series components, and we establish some asymptotic properties of the model selection procedure proposed. In addition, we demonstrate how to implement it by using both simulated and real examples. Our empirical studies show that the procedure works well.en
dc.description.statementofresponsibilityJiti Gao and Howell Tongen
dc.language.isoenen
dc.publisherBlackwell Publ Ltden
dc.source.urihttp://www3.interscience.wiley.com/journal/118808460/abstracten
dc.titleSemiparametric non-linear time series model selectionen
dc.typeJournal articleen
dc.identifier.rmid0020081884en
dc.identifier.doi10.1111/j.1369-7412.2004.05303.xen
dc.identifier.pubid42507-
pubs.library.collectionEconomics publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Economics publications

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