Journal
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Volume 47, Issue 5, Pages 1133-1154Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610926.2017.1316400
Keywords
Autoregressive; Bivariate; COM-Poisson; Dispersion; GQL; Non stationarity
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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.
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