期刊
REVIEW OF ECONOMICS AND STATISTICS
卷 83, 期 4, 页码 616-627出版社
M I T PRESS
DOI: 10.1162/003465301753237704
关键词
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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.
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