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Time Series of Count Data: Modelling and Estimation

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  • Jung, Robert
  • Kukuk, Martin
  • Liesenfeld, Roman

Abstract

This paper compares various models for time series of counts which can account for discreetness, overdispersion and serial correlation. Besides observation- and parameter-driven models based upon corresponding conditional Poisson distributions, we also consider a dynamic ordered probit model as a flexible specification to capture the salient features of time series of counts. For all models, we present appropriate efficient estimation procedures. For parameter-driven specifications this requires Monte Carlo procedures like simulated Maximum likelihood or Markov Chain Monte-Carlo. The methods including corresponding diagnostic tests are illustrated with data on daily admissions for asthma to a single hospital.

Suggested Citation

  • Jung, Robert & Kukuk, Martin & Liesenfeld, Roman, 2005. "Time Series of Count Data: Modelling and Estimation," Economics Working Papers 2005-08, Christian-Albrechts-University of Kiel, Department of Economics.
    • Handle: RePEc:zbw:cauewp:3194
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    16. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.
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    18. Maier, Ramona & Merz, Michael, 2008. "Credibility theory and filter theory in discrete and continuous time," Tübinger Diskussionsbeiträge 318, University of Tübingen, School of Business and Economics.

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    Keywords

    Efficient Importance Sampling; GLARMA; Markov Chain Monte-Carlo; Observation-driven model; Parameter-driven model; Ordered Probit;
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