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Time-series count data regression
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
1994 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 23, no 10, p. 2907-2925Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
1994. Vol. 23, no 10, p. 2907-2925
Keyword [en]
poisson regression, overdispersion, serial correlation, inference, maximum likelihood, least squares, method of moments
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-65003DOI: 10.1080/03610929408831424ISI: A1994PD48400012OAI: oai:DiVA.org:umu-65003DiVA, id: diva2:603196
Available from: 2013-02-05 Created: 2013-02-04 Last updated: 2018-03-15Bibliographically approved

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