Models for Count Data

Trivedi, Pravin K. (2014). Models for Count Data. In Anthony J. Culyer (Ed.), Encyclopedia of health economics (pp. 306-311) Amsterdam, Netherlands: Elsevier. doi:10.1016/B978-0-12-375678-7.00716-1

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Author Trivedi, Pravin K.
Title of chapter Models for Count Data
Title of book Encyclopedia of health economics
Place of Publication Amsterdam, Netherlands
Publisher Elsevier
Publication Year 2014
Sub-type Chapter in reference work, encyclopaedia, manual or handbook
DOI 10.1016/B978-0-12-375678-7.00716-1
Open Access Status
ISBN 9780123756794
Editor Anthony J. Culyer
Start page 306
End page 311
Total pages 6
Language eng
Abstract/Summary Many measures of health-care use that are analyzed and modeled in econometrics are event counts, for example, number of hospital admissions, doctor visits, emergency room visits. Event count models such as the Poisson regression is a common but restrictive starting point in many investigations. To overcome several key limitations of the Poisson regression model, a number of alternatives have been developed that are widely used. These include the negative binomial regression, the two-part model, the quantile count regression, and the latent class model. This article surveys popular modeling frameworks, associated issues of statistical inference, and their key features. Models for both cross-section and panel data are covered.
Q-Index Code BX
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Book Chapter
Collection: School of Economics Publications
 
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Created: Tue, 07 Oct 2014, 15:01:11 EST by Alys Hohnen on behalf of School of Economics