| Abstract: |
We 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.
We provide conditions under which the estimated distribution function
converges to the true distribution in the weak topology on the space of
distributions. We verify the consistency conditions for discrete choice,
continuous outcome and selection models. |