Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/21996
Authors: 
Jung, Robert
Kukuk, Martin
Liesenfeld, Roman
Year of Publication: 
2005
Series/Report no.: 
Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2005,08
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.
Subjects: 
Efficient Importance Sampling
GLARMA
Markov Chain Monte-Carlo
Observation-driven model
Parameter-driven model
Ordered Probit
Document Type: 
Working Paper

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