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Flexible Simulated Moment Estimation of Nonlinear Errors-in-Variables Models
Whitney K. Newey
The Review of Economics and Statistics
Vol. 83, No. 4 (Nov., 2001), pp. 616-627
Published by: The MIT Press
Stable URL: http://www.jstor.org/stable/3211757
Page Count: 12
You can always find the topics here!Topics: Estimators, Simulations, Instrumental variables estimation, Modeling, Statistical estimation, Economic modeling, Consistent estimators, Standard error, Statistical variance, Error rates
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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.
The Review of Economics and Statistics © 2001 The MIT Press