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Christian-Albrechts-Universität zu Kiel

Institut für Volkswirtschaftslehre - Department of Economics
Economics Working Papers

Economics Working Papers: Abstract 2005-08


Nummer

2005-08

Autoren

Robert Jung, Martin Kukuk, Roman Liesenfeld

 

Titel

 

Time Series of Count Data: Modelling and Estimation
Abstract This paper compares various models for time series of counts which can account for discreetness, over-dispersion 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

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