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Article: Diagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approach

TitleDiagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approach
Authors
KeywordsAbsolute residual autocorrelation
Asymptotic distribution
Diagnostic checking
GARCH model
Local least absolute deviation estimator
Squared residual autocorrelation
Issue Date2005
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2005, v. 92 n. 3, p. 691-701 How to Cite?
AbstractThe recent paper by Peng & Yao (2003) gave an interesting extension of least absolute deviation estimation to generalised autoregressive conditional heteroscedasticity, GARCH, time series models. The asymptotic distributions of absolute residual autocorrelations and squared residual autocorrelations from the GARCH model estimated by the least absolute deviation method are derived in this paper. These results lead to two useful diagnostic tools which can be used to check whether or not a GARCH model fitted by using the least absolute deviation method is adequate. Some simulation experiments give further support to the asymptotic theory and a real data example is also reported. © 2005 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/83013
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Gen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T08:35:56Z-
dc.date.available2010-09-06T08:35:56Z-
dc.date.issued2005en_HK
dc.identifier.citationBiometrika, 2005, v. 92 n. 3, p. 691-701en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/83013-
dc.description.abstractThe recent paper by Peng & Yao (2003) gave an interesting extension of least absolute deviation estimation to generalised autoregressive conditional heteroscedasticity, GARCH, time series models. The asymptotic distributions of absolute residual autocorrelations and squared residual autocorrelations from the GARCH model estimated by the least absolute deviation method are derived in this paper. These results lead to two useful diagnostic tools which can be used to check whether or not a GARCH model fitted by using the least absolute deviation method is adequate. Some simulation experiments give further support to the asymptotic theory and a real data example is also reported. © 2005 Biometrika Trust.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.rightsBiometrika. Copyright © Oxford University Press.en_HK
dc.subjectAbsolute residual autocorrelationen_HK
dc.subjectAsymptotic distributionen_HK
dc.subjectDiagnostic checkingen_HK
dc.subjectGARCH modelen_HK
dc.subjectLocal least absolute deviation estimatoren_HK
dc.subjectSquared residual autocorrelationen_HK
dc.titleDiagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approachen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=92&spage=691&epage=701&date=2005&atitle=Diagnostic+checking+for+time++series+models+with+conditional+heteroscedasticity+estimated+by+the+least+absolute+deviation+approachen_HK
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/92.3.691en_HK
dc.identifier.scopuseid_2-s2.0-24144460576en_HK
dc.identifier.hkuros110769en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-24144460576&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume92en_HK
dc.identifier.issue3en_HK
dc.identifier.spage691en_HK
dc.identifier.epage701en_HK
dc.identifier.isiWOS:000231524600014-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLi, G=52563850500en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.citeulike303901-

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