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Observation-driven models for Poisson counts

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Author Info
Richard A. Davis
Abstract

This paper is concerned with a general class of observation-driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modelling a wide range of dependence structures. Conditions for stationarity and ergodicity of these processes are established from which the large-sample properties of the maximum likelihood estimators can be derived. Simulations are provided to give additional insight into the finite-sample behaviour of the estimators. Finally an application to a regression model for daily counts of asthma presentations at a Sydney hospital is described. Copyright Biometrika Trust 2003, Oxford University Press.

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Publisher Info
Article provided by Oxford University Press for Biometrika Trust in its journal Biometrika.

Volume (Year): 90 (2003)
Issue (Month): 4 (December)
Pages: 777-790
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Handle: RePEc:oup:biomet:v:90:y:2003:i:4:p:777-790

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  1. Konstantinos Fokianos & Anders Rahbek & Dag Tjøstheim, 2008. "Poisson Autoregression," Discussion Papers 08-35, University of Copenhagen. Department of Economics, revised Dec 2008. [Downloadable!]
  2. Drew Creal & Siem Jan Koopman & André Lucas, 2008. "A General Framework for Observation Driven Time-Varying Parameter Models," Tinbergen Institute Discussion Papers 08-108/4, Tinbergen Institute. [Downloadable!]
    Other versions:
  3. Konstantinos Fokianos & Anders Rahbek & Dag Tjøstheim, 2009. "Poisson Autoregression," CREATES Research Papers 2009-12, School of Economics and Management, University of Aarhus. [Downloadable!]
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This page was last updated on 2009-11-28.

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