Time Series of Count Data: Modelling and Estimation

Robert Jung (), Martin Kukuk and Roman Liesenfeld

No 2005-08, Economics Working Papers from Christian-Albrechts-University of Kiel, Department of Economics

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.

Keywords: Efficient Importance Sampling; GLARMA; Markov Chain Monte-Carlo; Observation-driven model; Parameter-driven model; Ordered Probit (search for similar items in EconPapers)
Date: 2005
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ets
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cauewp:3194

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