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dc.contributor.authorPiancastelli, Luiza S. C.
dc.contributor.authorBarreto-Souza, Wagner
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2020-12-01T11:09:12Z
dc.date.available2020-12-01T11:09:12Z
dc.date.issued2020-11-17
dc.identifier.urihttp://hdl.handle.net/10754/666188
dc.description.abstractWe propose a novel flexible bivariate conditional Poisson (BCP) INteger-valued Generalized AutoRegressive Conditional Heteroscedastic (INGARCH) model for correlated count time series data. Our proposed BCP-INGARCH model is mathematically tractable and has as the main advantage over existing bivariate INGARCH models its ability to capture a broad range (both negative and positive) of contemporaneous cross-correlation which is a non-trivial advancement. Properties of stationarity and ergodicity for the BCP-INGARCH process are developed. Estimation of the parameters is performed through conditional maximum likelihood (CML) and finite sample behavior of the estimators are investigated through simulation studies. Asymptotic properties of the CML estimators are derived. Additional simulation studies compare and contrast methods of obtaining standard errors of the parameter estimates, where a bootstrap option is demonstrated to be advantageous. Hypothesis testing methods for the presence of contemporaneous correlation between the time series are presented and evaluated. We apply our methodology to monthly counts of hepatitis cases at two nearby Brazilian cities, which are highly cross-correlated. The data analysis demonstrates the importance of considering a bivariate model allowing for a wide range of contemporaneous correlation in real-life applications.
dc.description.sponsorshipL.S.C. Piancastelli thanks the financial support of Science Foundation Ireland under Grant number 18/CRT/6049. W. Barreto-Souza and H. Ombao would like to acknowledge the financial support by KAUST Research Fund and NIH 1R01EB028753-01. W. Barreto-Souza also thanks to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq-Brazil, grant number 305543/2018-0).
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2011.08799
dc.rightsArchived with thanks to arXiv
dc.titleFlexible Bivariate INGARCH Process With a Broad Range of Contemporaneous Correlation
dc.typePreprint
dc.contributor.departmentStatistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionSchool of Mathematics and Statistics, University College Dublin, Dublin, Ireland.
dc.identifier.arxivid2011.08799
kaust.personBarreto-Souza, Wagner
kaust.personOmbao, Hernando
refterms.dateFOA2020-12-01T11:10:31Z


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