Consistent estimation of zero-inflated count models
AbstractApplications of zero-inflated count data models have proliferated in health economics. However, zero-inflated Poisson or zero-inflated negative binomial maximum likelihood estimators are not robust to misspecification. This paper proposes Poisson quasi-likelihood estimators as an alternative. These estimators are consistent in the presence of excess zeros without having to specify the full distribution. The advantages of the Poisson quasi-likelihood approach are illustrated in a series of Monte Carlo simulations and in an application to the demand for health services.
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Bibliographic InfoPaper provided by Socioeconomic Institute - University of Zurich in its series SOI - Working Papers with number 0908.
Length: 21 pages
Date of creation: Jun 2009
Date of revision: Aug 2011
Excess zeros; Poisson; logit; unobserved heterogeneity; misspecification;
Other versions of this item:
- Kevin E. Staub & Rainer Winkelmann, 2013. "Consistent Estimation Of Zero‐Inflated Count Models," Health Economics, John Wiley & Sons, Ltd., vol. 22(6), pages 673-686, 06.
- I12 - Health, Education, and Welfare - - Health - - - Health Production
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
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