A BINAR(1) time-series model with cross-correlated COM–Poisson innovations |
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Authors: | V Jowaheer Y Sunecher |
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Institution: | 1. Department of Mathematics, Faculty of Science, University of Mauritius, Réduit, Moka, Mauritius;2. University of Technology, La Tour Koenig, Reduit, Mauritius |
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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. |
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Keywords: | Autoregressive Bivariate COM–Poisson Dispersion GQL Non stationarity |
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