File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: On a Mixture Autoregressive Conditional Heteroscedastic Model

TitleOn a Mixture Autoregressive Conditional Heteroscedastic Model
Authors
KeywordsAutocorrelation
EM algorithm
Model selection
Predictive distributions
Stationarity
Issue Date2001
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main
Citation
Journal Of The American Statistical Association, 2001, v. 96 n. 455, p. 982-995 How to Cite?
AbstractWe propose a mixture autoregressive conditional heteroscedastic (MAR-ARCH) model for modeling nonlinear time series. The models consist of a mixture of K autoregressive components with autoregressive conditional heteroscedasticity; that is, the conditional mean of the process variable follows a mixture AR (MAR) process, whereas the conditional variance of the process variable follows a mixture ARCH process. In addition to the advantage of better description of the conditional distributions from the MAR model, the MAR-ARCH model allows a more flexible squared autocorrelation structure. The stationarity conditions, autocorrelation function, and squared autocorrelation function are derived. Construction of multiple step predictive distributions is discussed. The estimation can be easily done through a simple EM algorithm, and the model selection problem is addressed. The shape-changing feature of the conditional distributions makes these models capable of modeling time series with multimodal conditional distributions and with heteroscedasticity. The models are applied to two real datasets and compared to other competing models. The MAR-ARCH models appear to capture features of the data better than the competing models.
Persistent Identifierhttp://hdl.handle.net/10722/82930
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWong, CSen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T08:35:00Z-
dc.date.available2010-09-06T08:35:00Z-
dc.date.issued2001en_HK
dc.identifier.citationJournal Of The American Statistical Association, 2001, v. 96 n. 455, p. 982-995en_HK
dc.identifier.issn0162-1459en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82930-
dc.description.abstractWe propose a mixture autoregressive conditional heteroscedastic (MAR-ARCH) model for modeling nonlinear time series. The models consist of a mixture of K autoregressive components with autoregressive conditional heteroscedasticity; that is, the conditional mean of the process variable follows a mixture AR (MAR) process, whereas the conditional variance of the process variable follows a mixture ARCH process. In addition to the advantage of better description of the conditional distributions from the MAR model, the MAR-ARCH model allows a more flexible squared autocorrelation structure. The stationarity conditions, autocorrelation function, and squared autocorrelation function are derived. Construction of multiple step predictive distributions is discussed. The estimation can be easily done through a simple EM algorithm, and the model selection problem is addressed. The shape-changing feature of the conditional distributions makes these models capable of modeling time series with multimodal conditional distributions and with heteroscedasticity. The models are applied to two real datasets and compared to other competing models. The MAR-ARCH models appear to capture features of the data better than the competing models.en_HK
dc.languageengen_HK
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=mainen_HK
dc.relation.ispartofJournal of the American Statistical Associationen_HK
dc.subjectAutocorrelationen_HK
dc.subjectEM algorithmen_HK
dc.subjectModel selectionen_HK
dc.subjectPredictive distributionsen_HK
dc.subjectStationarityen_HK
dc.titleOn a Mixture Autoregressive Conditional Heteroscedastic Modelen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0162-1459&volume=96&issue=455&spage=982&epage=995&date=2001&atitle=On+a+Mixture+Autoregressive+conditional+heteroscedastic+modelen_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1198/016214501753208645-
dc.identifier.scopuseid_2-s2.0-0442327786en_HK
dc.identifier.hkuros64633en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0442327786&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume96en_HK
dc.identifier.issue455en_HK
dc.identifier.spage982en_HK
dc.identifier.epage995en_HK
dc.identifier.isiWOS:000170729300026-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridWong, CS=36862846900en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats