Semiparametric estimation of random coefficients in structural economic models

In structural economic models, individuals are usually characterized as solving a decision problem that is governed by a finite set of parameters. This paper discusses the nonparametric estimation of the probability density function of these parameters if they are allowed to vary continuously across the population. We establish that the problem of recovering the probability density function of random parameters falls into the class of non-linear inverse problem. This framework helps us to answer the question whether there exist densities that satisfy this relationship. It also allows us to characterize the identified set of such densities. We obtain novel conditions for point identification, and establish that point identification is generically weak. Given this insight, we provide a consistent nonparametric estimator that accounts for this fact, and derive its asymptotic distribution. Our general framework allows us to deal with unobservable nuisance variables, e.g., measurement error, but also covers the case when there are no such nuisance variables. Finally, Monte Carlo experiments for several structural models are provided which illustrate the performance of our estimation procedure.