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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Count data models for demographic data. |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Mathematical Population Studies |
| Publisher | Taylor & Francis |
| Geographical Reach | international |
| ISSN | 0889-8480 |
| Volume | 4 |
| Number | 3 |
| Page Range | 205 - 221 |
| Date | 1994 |
| Abstract Text | Key demographic variables, such as the number of children and the number of marriages or divorces, can only take integer values. This papers deals with the estimation of single equation models in which the counts are regressed on a set of observed individual characteristics such as age, gender, or nationality. Most empirical work in population economics has neglected the fact that the dependent variable is a nonnegative integer. In the few cases where this feature was recognized, the authors advocated the use of the Poisson regression model. The Poisson model imposes, however, the equality of conditional mean and variance, a restriction which is often rejected by the data. We propose a generalized event count model to simultaneously allow for a wide class of count data models and account for over- and underdispersion. This model is successfully applied to German data on fertility, divorces and mobility. |
| Digital Object Identifier | 10.1080/08898489409525374 |
| PubMed ID | 12287090 |
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| Additional Information | This is an electronic version of an article published in Math Popul Stud 1994, 4(3):205-21, 223. Math Popul Stud is available online at http://www.informaworld.com/smpp/title~content=t713644738~db=all |