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ENDOGENEITY IN COUNT DATA MODELS: AN APPLICATION TO DEMAND FOR HEALTH CARE

✍ Scribed by F. A. G. WINDMEIJER; J. M. C. SANTOS SILVA

Publisher
John Wiley and Sons
Year
1997
Tongue
English
Weight
172 KB
Volume
12
Category
Article
ISSN
0883-7252

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✦ Synopsis

The generalized method of moments (GMM) estimation technique is discussed for count data models with endogenous regressors. Count data models can be speci®ed with additive or multiplicative errors. It is shown that, in general, a set of instruments is not orthogonal to both error types. Simultaneous equations with a dependent count variable often do not have a reduced form which is a simple function of the instruments. However, a simultaneous model with a count and a binary variable can only be logically consistent when the system is triangular. The GMM estimator is used in the estimation of a model explaining the number of visits to doctors, with as a possible endogenous regressor a self-reported binary health index. Further, a model is estimated, in stages, that includes latent health instead of the binary health index.

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