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Time-series count data regression
Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Nationalekonomi.
Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Nationalekonomi.
1994 (engelsk)Inngår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 23, nr 10, s. 2907-2925Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The count data model studied in the paper extends the Poisson model by allowing for overdispersion and serial correlation. Alternative approaches to estimate nuisance parameters, required for the correction of the Poisson maximum likelihood covariance matrix estimator and for a quasi-likelihood estimator, are studied. The estimators are evaluated by finite sample Monte Carlo experimentation. It is found that the Poisson maximum likelihood estimator with corrected covariance matrix estimators provide reliable inferences for longer time series. Overdispersion test statistics are wellbehaved, while conventional portmanteau statistics for white noise have too large sizes. Two empirical illustrations are included.

sted, utgiver, år, opplag, sider
1994. Vol. 23, nr 10, s. 2907-2925
Emneord [en]
poisson regression, overdispersion, serial correlation, inference, maximum likelihood, least squares, method of moments
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Identifikatorer
URN: urn:nbn:se:umu:diva-65003DOI: 10.1080/03610929408831424ISI: A1994PD48400012OAI: oai:DiVA.org:umu-65003DiVA, id: diva2:603196
Tilgjengelig fra: 2013-02-05 Laget: 2013-02-04 Sist oppdatert: 2018-06-08bibliografisk kontrollert

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