A discrete random effects probit model with application to the demand for preventive care
开发了一种随机截距分布由离散密度近似的随机效应Probit模型，蒙特卡洛模拟显示仅需3-4个支撑点即可准确估计参数，实证表明家庭特征和未观测异质性共同影响预防性医疗需求。
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.