A discrete random effects probit model with application to the demand for preventive care.
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.
Deb
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Department of Economics, Indiana University-Purdue University Indianapolis, IN 46202, USA. pdeb@iupui.edu
D000704:Analysis of Variance; D015982:Bias; D016009:Chi-Square Distribution; D005190:Family / Q000523:psychology; D020342:Finite Element Analysis; D019538:Health Care Surveys; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D020380:Needs Assessment / Q000706:statistics & numerical data*; D011314:Preventive Health Services / Q000706:statistics & numerical data*; D013269:Stochastic Processes