Interventions in ingarch processes
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Date
2009-08-05T10:02:48Z
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Abstract
We study the problem of intervention effects generating various types of outliers in a linear count time series
model. This model belongs to the class of observation driven models and extends the class of Gaussian
linear time series models within the exponential family framework. Studies about effects of covariates and
interventions for count time series models 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 parametric bootstrap. The usefulness of the
proposed methods is illustrated using simulated and real data examples.
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generalized linear models, level shifts, observation driven models, outliers, parametric bootstrap, transient shifts