Generalized binary vector autoregressive processes

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dc.contributor.authorJentsch, Carsten
dc.contributor.authorReichmann, Lena
dc.date.accessioned2022-03-22T14:46:26Z
dc.date.available2022-03-22T14:46:26Z
dc.date.issued2021-07-28
dc.description.abstractVector-valued extensions of univariate generalized binary auto-regressive (gbAR) processes are proposed that enable the joint modeling of serial and cross-sectional dependence of multi-variate binary data. The resulting class of generalized binary vector auto-regressive (gbVAR) models is parsimonious, nicely interpretable and allows also to model negative dependence. We provide stationarity conditions and derive moving-average-type representations that allow to prove geometric mixing properties. Furthermore, we derive general stochastic properties of gbVAR processes, including formulae for transition probabilities. In particular, classical Yule–Walker equations hold that facilitate parameter estimation in gbVAR models. In simulations, we investigate the estimation performance, and for illustration, we apply gbVAR models to particulate matter (PM10, ‘fine dust’) alarm data observed at six monitoring stations in Stuttgart, Germany.en
dc.identifier.urihttp://hdl.handle.net/2003/40814
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22671
dc.language.isoende
dc.relation.ispartofseriesJournal of time series analysis;43(2)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBinary dataen
dc.subjectMixing propertiesen
dc.subjectMulti-variate time seriesen
dc.subjectStationarity conditionsen
dc.subjectTransition probabilitiesen
dc.subjectYule-Walker equationsen
dc.subject.ddc310
dc.titleGeneralized binary vector autoregressive processesen
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationJentsch, C. and Reichmann, L. (2022), Generalized binary vector autoregressive processes. J. Time Ser. Anal., 43: 285-311. https://doi.org/10.1111/jtsa.12614de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1111/jtsa.12614de

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