A BINAR(1) time-series model with cross-correlated COM–Poisson innovations. Issue 5 (4th March 2018)

• Record Type: Journal Article
• Authors: Jowaheer, V.; Khan, N. Mamode; Sunecher, Y.
• Is Part Of: Communications in statistics. Volume 47:Issue 5(2018)
• Page Start: 1133
• Page End: 1154
• Publication Date: 2018-03-04
• Subjects: Autoregressive -- Bivariate -- COM–Poisson -- Dispersion -- GQL -- Non stationarity; 62HXX -- 62FXX; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2017.1316400
• Languages: English
• ISSNs: 0361-0926

Abstract

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.