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Extensions of hurdle models for overdispersed count data.
1Max Planck Society, Munich Center for the Economics of Aging, Munich, Germany.
Health Economics
|December 11, 2012
Summary
This study introduces flexible extensions to hurdle models for analyzing count data. These new models relax common assumptions, offering improved analytical capabilities for researchers.
Area of Science:
- Econometrics
- Statistical modeling
- Health economics
Background:
- Hurdle models are standard for count data analysis.
- Existing models often rely on restrictive assumptions.
- Recent statistical developments allow for more flexible modeling.
Purpose of the Study:
- To develop novel extensions of hurdle models.
- To enhance the flexibility of popular count data specifications.
- To provide testable nested models for broader applicability.
Main Methods:
- Development of two new hurdle model extensions.
- Utilizing recent advancements in count data literature.
- Ensuring new models nest existing specifications for parametric testing.
Main Results:
- The proposed extensions offer greater flexibility than traditional hurdle models.
- The nested structure allows for formal testing against simpler models.
- Demonstrated applicability in a health economics context.
Conclusions:
- The developed hurdle model extensions provide more adaptable tools for count data.
- These models relax restrictive assumptions, enhancing analytical power.
- The findings are relevant for researchers in econometrics and health economics.
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