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Time Series of Count Data : Modelling and Estimation Author info | Abstract | Publisher info | Download info | Related research | Statistics Jung, Robert
Kukuk, Martin
Liesenfeld, Roman
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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. --
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Paper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number
2005,08.
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Date of creation: 2005Date of revision:
Handle: RePEc:zbw:cauewp:3194Contact details of provider: Web page: http://www.wiso.uni-kiel.de/econ/
For technical questions regarding this item, or to correct its listing, contact: (ZBW - German National Library for Economics).
Keywords: Efficient Importance Sampling ; GLARMA ; Markov Chain Monte-Carlo ; Observation-driven model ; Parameter-driven model ; Ordered Probit ; Other versions of this item:
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references Cited by : (explanations , Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.)
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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