Estimation of count data models with endogenous regressors; an application to demand for health care
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vor 28 Jahren
The generalized method of moments (GMM) estimation technique is
discussed for count data models with endogenous regressors. Count
data models can be specified 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 recursive. 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.
discussed for count data models with endogenous regressors. Count
data models can be specified 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 recursive. 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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