Time-series count data regression
1994 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 23, no 10, 2907-2925 p.Article in journal (Refereed) Published
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, 2907-2925 p.
poisson regression, overdispersion, serial correlation, inference, maximum likelihood, least squares, method of moments
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:umu:diva-65003DOI: 10.1080/03610929408831424ISI: A1994PD48400012OAI: oai:DiVA.org:umu-65003DiVA: diva2:603196