File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Book Chapter: Time Series: Advanced methods

TitleTime Series: Advanced methods
Authors
Issue Date2002
PublisherElsevier Science B.V..
Citation
Time Series: Advanced methods. In International Encyclopedia of the Social & Behavioral Sciences, v. 23, p. 15699-15704. New York, U.S.A.: Elsevier Science B.V., 2002 How to Cite?
AbstractRecent developments in the time-domain analysis of time series are reviewed. The concept of dynamical systems serves as a unifying theme of the review. We consider first methods for the modelling of the drift component or the conditional mean of a time series. This includes the class of threshold models and its variants. We then consider methods for the modelling of the diffusion component or the conditional variance of a time series which includes the popular generalized autoregressive conditional heteroscedastic G(ARCH) models and the stochastic volatility (SV) models. Hybrid models for the modelling of both the drift and the diffusion are then introduced. Long memory and discrete-valued time series models are also included. The main focus is on univariate series although multivariate series are also mentioned where appropriate.
Persistent Identifierhttp://hdl.handle.net/10722/120710
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLi, WKen_HK
dc.contributor.authorTong, Hen_HK
dc.date.accessioned2010-09-26T09:52:39Z-
dc.date.available2010-09-26T09:52:39Z-
dc.date.issued2002en_HK
dc.identifier.citationTime Series: Advanced methods. In International Encyclopedia of the Social & Behavioral Sciences, v. 23, p. 15699-15704. New York, U.S.A.: Elsevier Science B.V., 2002en_HK
dc.identifier.isbn0080430767-
dc.identifier.urihttp://hdl.handle.net/10722/120710-
dc.description.abstractRecent developments in the time-domain analysis of time series are reviewed. The concept of dynamical systems serves as a unifying theme of the review. We consider first methods for the modelling of the drift component or the conditional mean of a time series. This includes the class of threshold models and its variants. We then consider methods for the modelling of the diffusion component or the conditional variance of a time series which includes the popular generalized autoregressive conditional heteroscedastic G(ARCH) models and the stochastic volatility (SV) models. Hybrid models for the modelling of both the drift and the diffusion are then introduced. Long memory and discrete-valued time series models are also included. The main focus is on univariate series although multivariate series are also mentioned where appropriate.-
dc.languageengen_HK
dc.publisherElsevier Science B.V..en_HK
dc.relation.ispartofInternational Encyclopedia of the Social & Behavioral Sciencesen_HK
dc.titleTime Series: Advanced methodsen_HK
dc.typeBook_Chapteren_HK
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hken_HK
dc.identifier.emailTong, H: howell.tong@gmail.comen_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.identifier.doi10.1016/B978-0-08-097086-8.42092-1-
dc.identifier.hkuros66717en_HK
dc.identifier.spage15699en_HK
dc.identifier.epage15704en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats