Poisson Autoregression

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  • DP 08-35

    Final published version, 534 KB, PDF document

Konstantinos Fokianos, Anders Christian Rahbek, Dag Tjøstheim

This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional variance, implying an interpretation as an integer valued GARCH process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model for time series is considered. Under geometric ergodicity the maximum likelihood estimators of the parameters are shown to be asymptotically Gaussian in the linear model. In addition we provide a consistent estimator of the asymptotic covariance, which is used in the simulations and the analysis of some transaction data. Our approach to verifying geometric ergodicity proceeds via Markov theory and irreducibility. Finding transparent conditions for proving ergodicity turns out to be a delicate problem in the original model formulation. This problem is circumvented by allowing a perturbation of the model. We show that as the perturbations can be chosen to be arbitrarily small, the differences between the perturbed and non-perturbed versions vanish as far as the asymptotic distribution of the parameter estimates is concerned.
Original languageEnglish
PublisherDepartment of Economics, University of Copenhagen
Number of pages37
Publication statusPublished - 2008

    Research areas

  • Faculty of Social Sciences - generalized linear models, non-canonical link function, count data, Poisson regression, likelihood, geometric ergodicity, integer GARCH, observation driven models, asymptotic theory

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