Simulation and the Asymptotics of Optimization Estimators

Abstract

A general central limit theorem is proved for estimators defined by minimization of the length of a vector-valued, random criterion function. No smoothness assumptions are imposed on the criterion function in order that the results might apply to a broad class of simulation estimators. Complete analyses of two simulation estimators, one introduced by A. Pakes (1986) and the other by D. McFadden (1989), illustrate the application of the general theorems. These examples illustrate how simulation can be used to circumvent two computational problems that arise frequently in applied econometrics: evaluating intractable aggregation formulae and evaluating discrete response probabilities. Copyright 1989 by The Econometric Society.