A Simple Nonparametric Approach To Estimating The Distribution Of Random Coefficients In Structural Models
The authors explore a nonparametric mixtures estimator for recovering the joint distribution of random coefficients in economic models. The estimator is based on linear regression subject to linear inequality constraints and is computationally attractive compared to alternative, nonparametric estimators. They provide conditions under which the estimated distribution function converges to the true distribution in the weak topology on the space of distributions. They verify the consistency conditions for discrete choice, continuous outcome and selection models.