We propose a simple mixtures estimator for recovering the joint distribution of parameter heterogeneity in economic models, such as the random coefficients logit. The estimator is based on linear regression subject to linear inequality constraints, and is robust, easy to program, and computationally attractive compared to alternative estimators for random coefficient models. For complex structural models, one does not need to nest a solution to the economic model during optimization. We present a Monte Carlo study and an empirical application to dynamic programming discrete choice with a serially correlated unobserved state variable.
Authors: Jeremy Fox,Kyoo-il Kim, Stephen P. Ryan, and Patrick Bajari
Quantitative Economics, Vol.2, No. 3 (2011), pp. 381–418. (link)