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Count data models for demographic data
Mathematical Population Studies
|February 1, 1994
Summary
This study introduces a flexible generalized event count model for analyzing count data, addressing over- and underdispersion. The model was effectively applied to German demographic data, including fertility, divorces, and mobility.
Area of Science:
- Econometrics
- Demography
- Statistical Modeling
Background:
- Estimating single equation models with count data requires specialized approaches.
- Standard count models may not adequately handle overdispersion or underdispersion.
- Individual characteristics influence count outcomes.
Purpose of the Study:
- To propose a generalized event count model for flexible count data analysis.
- To simultaneously accommodate various count data models.
- To account for overdispersion and underdispersion in regression models.
Main Methods:
- Development of a generalized event count model.
- Simultaneous allowance for a wide class of count data models.
- Application to German demographic data (fertility, divorces, mobility).
Main Results:
- The proposed generalized event count model effectively handles count data.
- The model successfully accounts for both overdispersion and underdispersion.
- Empirical application demonstrates the model's utility on real-world demographic data.
Conclusions:
- The generalized event count model offers a robust framework for count data analysis.
- This approach provides a unified way to model various count data phenomena.
- The model is suitable for analyzing demographic events like fertility, divorces, and mobility.
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