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Outliers and interventions in INGARCH time series

We consider intervention effects generating various types of outliers in a linear count time series model which belongs to the class of observation driven models. Such models are widely used, because they extend the class of Gaussian linear time series models in a natural way. However, studies about effects of covariates and interventions have largely fallen behind due to the fact that the underlying process, whose behavior determines the dynamics of the observed process, is not observed. We suggest a computationally feasible approach to these problems, focusing especially on the detection and estimation of sudden shifts and outliers. To identify successfully such unusual events we employ the maximum of score tests, whose critical values in finite samples are determined by simulation. The usefulness of the proposed methods is illustrated using simulated and real data examples from epidemiology, and comments are given on robust estimation. References R. Ferland, A. Latour, and D. Oraichi (2006). Integer–valued GARCH processes. Journal of Time Series Analysis, 27, 923–942. K. Fokianos, A. Rahbek, and D. Tjøstheim (2009). Poisson autoregression. Working paper. K. Fokianos, and R. Fried (2009). Interventions in INGARCH Processes. Working paper.