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Article: Estimation of nonlinear time series with conditional heteroscedastic variances by iteratively weighted least squares

TitleEstimation of nonlinear time series with conditional heteroscedastic variances by iteratively weighted least squares
Authors
KeywordsAutoregressive conditional heteroscedasticity
Iteratively weighted least squares
Maximum likelihood estimation
Nonlinear time series models
Issue Date1997
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 1997, v. 24 n. 2, p. 169-178 How to Cite?
AbstractIn this paper we consider a unified approach for fitting conditionally nonlinear time series models with heteroscedastic variances. The model considered is completely general, requiring only that the forms of the mean and conditional variance functions be specified. Based on the recent results of Mak (1993) on general estimating equations, we derive a convenient expression for the conditional information matrix. Furthermore, it is shown that estimation in such models can be performed via an iteratively weighted least squares algorithm (IWLS), so that the computational problems involved can be conveniently handled by many popular statistical packages. Its implementation is numerically illustrated using the "threshold plus ARCH" model. The algorithm is also demonstrated using both simulated and real data to be superior to the popular BHHH algorithm, which requires a much longer computing time and fails to converge if initial values are not chosen properly.
Persistent Identifierhttp://hdl.handle.net/10722/83048
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMak, TKen_HK
dc.contributor.authorWong, Hen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T08:36:19Z-
dc.date.available2010-09-06T08:36:19Z-
dc.date.issued1997en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 1997, v. 24 n. 2, p. 169-178en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/83048-
dc.description.abstractIn this paper we consider a unified approach for fitting conditionally nonlinear time series models with heteroscedastic variances. The model considered is completely general, requiring only that the forms of the mean and conditional variance functions be specified. Based on the recent results of Mak (1993) on general estimating equations, we derive a convenient expression for the conditional information matrix. Furthermore, it is shown that estimation in such models can be performed via an iteratively weighted least squares algorithm (IWLS), so that the computational problems involved can be conveniently handled by many popular statistical packages. Its implementation is numerically illustrated using the "threshold plus ARCH" model. The algorithm is also demonstrated using both simulated and real data to be superior to the popular BHHH algorithm, which requires a much longer computing time and fails to converge if initial values are not chosen properly.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.rightsComputational Statistics & Data Analysis. Copyright © Elsevier BV.en_HK
dc.subjectAutoregressive conditional heteroscedasticityen_HK
dc.subjectIteratively weighted least squaresen_HK
dc.subjectMaximum likelihood estimationen_HK
dc.subjectNonlinear time series modelsen_HK
dc.titleEstimation of nonlinear time series with conditional heteroscedastic variances by iteratively weighted least squaresen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-9473&volume=24&spage=169&epage=178&date=1997&atitle=Estimation+of+nonlinear+time+series+with+conditional+heteroscedastic+variances+by+iteratively+weighted+least+squaresen_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S0167-9473(96)00060-6-
dc.identifier.scopuseid_2-s2.0-0031550927en_HK
dc.identifier.hkuros21992en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0031550927&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue2en_HK
dc.identifier.spage169en_HK
dc.identifier.epage178en_HK
dc.identifier.isiWOS:A1997WR18800003-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridMak, TK=7401931097en_HK
dc.identifier.scopusauthoridWong, H=7402864953en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK

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