A discrete random effects probit model with application to the demand for preventive care

Abstract

I have developed a random effects probit model in which the distribution of the random intercept is approximated by a discrete density. Monte Carlo results show that only three to four points of support are required for the discrete density to closely mimic normal and chi‐squared densities and provide unbiased estimates of the structural parameters and the variance of the random intercept. The empirical application shows that both observed family characteristics and unobserved family‐level heterogeneity are important determinants of the demand for preventive care. Copyright © 2001 John Wiley & Sons, Ltd.