Show simple item record

dc.contributor.authorFokianos, Konstantinos
dc.contributor.authorStøve, Bård
dc.contributor.authorTjøstheim, Dag Bjarne
dc.contributor.authorDoukhan, Paul
dc.date.accessioned2021-05-10T09:43:45Z
dc.date.available2021-05-10T09:43:45Z
dc.date.created2020-09-02T12:24:04Z
dc.date.issued2020
dc.PublishedBernoulli. 2020, 26 (1), 471-499.
dc.identifier.issn1350-7265
dc.identifier.urihttps://hdl.handle.net/11250/2754585
dc.description.abstractWe are studying linear and log-linear models for multivariate count time series data with Poisson marginals. For studying the properties of such processes we develop a novel conceptual framework which is based on copulas. Earlier contributions impose the copula on the joint distribution of the vector of counts by employing a continuous extension methodology. Instead we introduce a copula function on a vector of associated continuous random variables. This construction avoids conceptual difficulties related to the joint distribution of counts yet it keeps the properties of the Poisson process marginally. Furthermore, this construction can be employed for modeling multivariate count time series with other marginal count distributions. We employ Markov chain theory and the notion of weak dependence to study ergodicity and stationarity of the models we consider. Suitable estimating equations are suggested for estimating unknown model parameters. The large sample properties of the resulting estimators are studied in detail. The work concludes with some simulations and a real data example.en_US
dc.language.isoengen_US
dc.publisherBernoulli Society for Mathematical Statistics and Probabilityen_US
dc.titleMultivariate count autoregressionen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2020 ISI/BSen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.3150/19-BEJ1132
dc.identifier.cristin1826707
dc.source.journalBernoullien_US
dc.source.4026
dc.source.141
dc.source.pagenumber471-499en_US
dc.identifier.citationBernoulli. 2020, 26(1): 471-499en_US
dc.source.volume26en_US
dc.source.issue1en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record