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Article: Estimation for partially nonstationary multivariate autoregressive models with conditional heteroscedasticity

TitleEstimation for partially nonstationary multivariate autoregressive models with conditional heteroscedasticity
Authors
KeywordsBrownian motion
Cointegration
Full-rank and reduced-rank maximum likelihood estimators
Least squares estimator
Multivariate ARCH process
Partially nonstationary
Unit root
Issue Date2001
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2001, v. 88 n. 4, p. 1135-1152 How to Cite?
AbstractThis paper investigates a partially nonstationary multivariate autoregressive model, which allows its innovations to be generated by a multivariate ARCH, autoregressive conditional heteroscedastic, process. Three estimators, including the least squares estimator, a full-rank maximum likelihood estimator and a reduced-rank maximum likelihood estimator, are considered and their asymptotic distributions are derived. When the multivariate ARCH process reduces to the innovation with a constant covariance matrix, these asymptotic distributions are the same as those given by Ahn & Reinsel (1990). However, in the presence of multivariate ARCH innovations, the asymptotic distributions of the full-rank maximum likelihood estimator and the reduced-rank maximum likelihood estimator involve two correlated multivariate Brownian motions, which are different from those given by Ahn & Reinsel (1990). Simulation results show that the full-rank and reduced-rank maximum likelihood estimator are more efficient than the least squares estimator. An empirical example shows that the two features of multivariate conditional heteroscedasticity and partial nonstationarity may be present simultaneously in a multivariate time series. © 2001 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/83043
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLi, WKen_HK
dc.contributor.authorLing, Sen_HK
dc.contributor.authorWong, Hen_HK
dc.date.accessioned2010-09-06T08:36:16Z-
dc.date.available2010-09-06T08:36:16Z-
dc.date.issued2001en_HK
dc.identifier.citationBiometrika, 2001, v. 88 n. 4, p. 1135-1152en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/83043-
dc.description.abstractThis paper investigates a partially nonstationary multivariate autoregressive model, which allows its innovations to be generated by a multivariate ARCH, autoregressive conditional heteroscedastic, process. Three estimators, including the least squares estimator, a full-rank maximum likelihood estimator and a reduced-rank maximum likelihood estimator, are considered and their asymptotic distributions are derived. When the multivariate ARCH process reduces to the innovation with a constant covariance matrix, these asymptotic distributions are the same as those given by Ahn & Reinsel (1990). However, in the presence of multivariate ARCH innovations, the asymptotic distributions of the full-rank maximum likelihood estimator and the reduced-rank maximum likelihood estimator involve two correlated multivariate Brownian motions, which are different from those given by Ahn & Reinsel (1990). Simulation results show that the full-rank and reduced-rank maximum likelihood estimator are more efficient than the least squares estimator. An empirical example shows that the two features of multivariate conditional heteroscedasticity and partial nonstationarity may be present simultaneously in a multivariate time series. © 2001 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.subjectBrownian motionen_HK
dc.subjectCointegrationen_HK
dc.subjectFull-rank and reduced-rank maximum likelihood estimatorsen_HK
dc.subjectLeast squares estimatoren_HK
dc.subjectMultivariate ARCH processen_HK
dc.subjectPartially nonstationaryen_HK
dc.subjectUnit rooten_HK
dc.titleEstimation for partially nonstationary multivariate autoregressive models with conditional heteroscedasticityen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=88&issue=4&spage=1135&epage=1152&date=2001&atitle=Estimation+for+partially+nonstationary+multivariate+autoregressive+models+with+conditional+heteroscedasticityen_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/biomet/88.4.1135-
dc.identifier.scopuseid_2-s2.0-0037862848en_HK
dc.identifier.hkuros65158en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037862848&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume88en_HK
dc.identifier.issue4en_HK
dc.identifier.spage1135en_HK
dc.identifier.epage1152en_HK
dc.identifier.isiWOS:000172541700016-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.scopusauthoridLing, S=7102701223en_HK
dc.identifier.scopusauthoridWong, H=7402864953en_HK

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