Endogeneity in count data models: An application to demand for health care

Frank Windmeijer, JMCS Silva

Research output: Contribution to journalArticle (Academic Journal)peer-review

164 Citations (Scopus)

Abstract

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 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. (C) 1997 by John Wiley & Sons, Ltd.

Original languageEnglish
Pages (from-to)281-294
Number of pages14
JournalJournal of Applied Econometrics
Volume12
Issue number3
Publication statusPublished - 1997

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