Flexible Simulated Moment Estimation of Nonlinear Errors-in-Variables Models

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Abstract
Nonlinear regression with measurement error is important for estimation from microeconomic data. One approach to identification and estimation is a causal model, in which the unobserved true variable is predicted by observable variables. This paper details the estimation of such a model using simulated moments and a flexible disturbance distribution. An estimator of the asymptotic variance is given for parametric models. Also, a semiparametric consistency result is given. The value of the estimator is demonstrated in a Monte Carlo study and an application to estimating Engel Curves. © 2001 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology (This abstract was borrowed from another version of this item.)
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