Intervention analysis for integer-valued autoregressive models
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We study the problem of intervention effects generating various types of outliers in an integer-valued autoregressive model with Poisson innovations. We concentrate on outliers which enter the dynamics and can be seen as effects of extraordinary events. We consider three different scenarios, namely the detection of an intervention effect of a known type at a known time, the detection of an intervention effect of unknown type at a known time and the detection of an intervention effect when both the type and the time are unknown. We develop F-tests and score tests for the first scenario. For the second and third scenarios we rely on the maximum of the different F-type or score statistics. The usefulness of the proposed approach is illustrated using monthly data on human brucellosis infections in Greece.
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