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Article: Self-Excited Threshold Poisson Autoregression

TitleSelf-Excited Threshold Poisson Autoregression
Authors
Issue Date2014
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, 2014, v. 109 n. 506, p. 777-787 How to Cite?
AbstractThis article studies theory and inference of an observation-driven model for time series of counts. It is assumed that the observations follow a Poisson distribution conditioned on an accompanying intensity process, which is equipped with a two-regime structure according to the magnitude of the lagged observations. Generalized from the Poisson autoregression, it allows more flexible, and even negative correlation, in the observations, which cannot be produced by the single-regime model. Classical Markov chain theory and Lyapunov’s method are used to derive the conditions under which the process has a unique invariant probability measure and to show a strong law of large numbers of the intensity process. Moreover, the asymptotic theory of the maximum likelihood estimates of the parameters is established. A simulation study and a real-data application are considered, where the model is applied to the number of major earthquakes in the world. Supplementary materials for this article are available online.
Persistent Identifierhttp://hdl.handle.net/10722/200917
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Cen_US
dc.contributor.authorLiu, Hen_US
dc.contributor.authorYao, JJen_US
dc.contributor.authorDavis, RAen_US
dc.contributor.authorLi, WKen_US
dc.date.accessioned2014-08-21T07:07:09Z-
dc.date.available2014-08-21T07:07:09Z-
dc.date.issued2014en_US
dc.identifier.citationJournal of the American Statistical Association, 2014, v. 109 n. 506, p. 777-787en_US
dc.identifier.urihttp://hdl.handle.net/10722/200917-
dc.description.abstractThis article studies theory and inference of an observation-driven model for time series of counts. It is assumed that the observations follow a Poisson distribution conditioned on an accompanying intensity process, which is equipped with a two-regime structure according to the magnitude of the lagged observations. Generalized from the Poisson autoregression, it allows more flexible, and even negative correlation, in the observations, which cannot be produced by the single-regime model. Classical Markov chain theory and Lyapunov’s method are used to derive the conditions under which the process has a unique invariant probability measure and to show a strong law of large numbers of the intensity process. Moreover, the asymptotic theory of the maximum likelihood estimates of the parameters is established. A simulation study and a real-data application are considered, where the model is applied to the number of major earthquakes in the world. Supplementary materials for this article are available online.-
dc.languageengen_US
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=mainen_US
dc.relation.ispartofJournal of the American Statistical Associationen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleSelf-Excited Threshold Poisson Autoregressionen_US
dc.typeArticleen_US
dc.identifier.emailYao, JJ: jeffyao@hku.hken_US
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hken_US
dc.identifier.authorityYao, JJ=rp01473en_US
dc.identifier.authorityLi, WK=rp00741en_US
dc.description.naturepostprint-
dc.identifier.doi10.1080/01621459.2013.872994-
dc.identifier.scopuseid_2-s2.0-84907885684-
dc.identifier.hkuros232860en_US
dc.identifier.volume109en_US
dc.identifier.spage777en_US
dc.identifier.epage787en_US
dc.identifier.isiWOS:000338236000026-
dc.publisher.placeUSAen_US

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