Please use this identifier to cite or link to this item:
Fokianos, Konstantions
Fried, Roland
Year of Publication: 
Series/Report no.: 
Technical Report 2009,11
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.
parametric bootstrap
generalized linear models
observation driven models
level shifts
transient shifts
Document Type: 
Working Paper

Files in This Item:
415.91 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.