Arbeitspapier
Time Series of Count Data: Modelling and Estimation
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Economics Working Paper ; No. 2005-08
- Klassifikation
-
Wirtschaft
- Thema
-
Efficient Importance Sampling
GLARMA
Markov Chain Monte-Carlo
Observation-driven model
Parameter-driven model
Ordered Probit
Zeitreihenanalyse
Zähldatenmodell
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Jung, Robert
Kukuk, Martin
Liesenfeld, Roman
- Ereignis
-
Veröffentlichung
- (wer)
-
Kiel University, Department of Economics
- (wo)
-
Kiel
- (wann)
-
2005
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Jung, Robert
- Kukuk, Martin
- Liesenfeld, Roman
- Kiel University, Department of Economics
Entstanden
- 2005
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