Communication in Statistics- Theory and Methods · 2017 · 18 citations · 43 references
BusinessBiostatisticsEconometricsStochastic ModelingTime Series AnalysisCount Data ModelsDependence ModelingPoisson ProcessesStationarity AnalysisTime Series EconometricsTime Series ModelingAutoregressive ModelsAutoregressive Time Series ModelsBivariate Count ModelsCount Time Series AnalysisCount Time Series ModelsInteger-valued Autoregressive ModelsNon-stationary Time SeriesNonstationary Time Series Models
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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