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Article: Testing a linear time series model against its threshold extension

TitleTesting a linear time series model against its threshold extension
Authors
KeywordsAutoregressive moving average model
Bootstrap method
Quasilikelihood ratio test
Threshold model
Issue Date2011
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2011, v. 98 n. 1, p. 243-250 How to Cite?
AbstractThis paper derives the asymptotic null distribution of a quasilikelihood ratio test statistic for an autoregressive moving average model against its threshold extension. The null hypothesis is that of no threshold, and the error term could be dependent. The asymptotic distribution is rather complicated, and all existing methods for approximating a distribution in the related literature fail to work. Hence, a novel bootstrap approximation based on stochastic permutation is proposed in this paper. Besides being robust to the assumptions on the error term, our method enjoys more flexibility and needs less computation when compared with methods currently used in the literature. Monte Carlo experiments give further support to the new approach, and an illustration is reported. © 2011 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/133263
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Research Grant Council
Funding Information:

The authors thank the editor and two referees for comments that led to the substantial improvement of this paper, and the Hong Kong Research Grant Council for partial support.

References

 

DC FieldValueLanguage
dc.contributor.authorLi, Gen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2011-05-06T06:05:37Z-
dc.date.available2011-05-06T06:05:37Z-
dc.date.issued2011en_HK
dc.identifier.citationBiometrika, 2011, v. 98 n. 1, p. 243-250en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/133263-
dc.description.abstractThis paper derives the asymptotic null distribution of a quasilikelihood ratio test statistic for an autoregressive moving average model against its threshold extension. The null hypothesis is that of no threshold, and the error term could be dependent. The asymptotic distribution is rather complicated, and all existing methods for approximating a distribution in the related literature fail to work. Hence, a novel bootstrap approximation based on stochastic permutation is proposed in this paper. Besides being robust to the assumptions on the error term, our method enjoys more flexibility and needs less computation when compared with methods currently used in the literature. Monte Carlo experiments give further support to the new approach, and an illustration is reported. © 2011 Biometrika Trust.en_HK
dc.languageeng-
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.subjectAutoregressive moving average modelen_HK
dc.subjectBootstrap methoden_HK
dc.subjectQuasilikelihood ratio testen_HK
dc.subjectThreshold modelen_HK
dc.titleTesting a linear time series model against its threshold extensionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=98&issue=1&spage=243&epage=250&date=2011&atitle=Testing+a+linear+time+series+models+against+its+threshold+extension-
dc.identifier.emailLi, G: gdli@hku.hken_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, G=rp00738en_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/biomet/asq074en_HK
dc.identifier.scopuseid_2-s2.0-79952178737en_HK
dc.identifier.hkuros184614-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952178737&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume98en_HK
dc.identifier.issue1en_HK
dc.identifier.spage243en_HK
dc.identifier.epage250en_HK
dc.identifier.eissn1464-3510-
dc.identifier.isiWOS:000287759000020-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLi, G=52563850500en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.citeulike8947560-

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