Flexible simulated moment estimation of nonlinear errors-in-variables models

Nonlinear regression with measurement error is important for estimation fro m microeconomic data. One approach to identification and estimation is a ca usal model, in which the unobserved true variable is predicted by observabl e variables. This paper details the estimation of such a model using simula ted moments and a flexible disturbance distribution. An estimator of the as ymptotic variance is given for parametric models. Also, a semiparametric co nsistency result is given. The value of the estimator is demonstrated in a Monte Carlo study and an application to estimating Engel Curves.