A BINAR(1) time-series model with cross-correlated COM–Poisson innovations

V. Jowaheer, N. Mamode Khan and Y. Sunecher

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 5, 1133-1154

Abstract: This article proposes a bivariate integer-valued autoregressive time-series model of order 1 (BINAR(1) with COM–Poisson marginals to analyze a pair of non stationary time series of counts. The interrelation between the series is induced by the correlated innovations, while the non stationarity is captured through a common set of time-dependent covariates that influence the count responses. The regression and dependence effects are estimated using generalized quasi-likelihood (GQL) approach. Simulation experiments are performed to assess the performance of the estimation algorithms. The proposed BINAR(1) process is applied to analyze a real-life series of day and night accidents in Mauritius.

Date: 2018
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DOI: 10.1080/03610926.2017.1316400

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