Flexible Simulated Moment Estimation Of Nonlinear Errors-In-Variables Models
AbstractNonlinear 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
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Bibliographic InfoArticle provided by MIT Press in its journal The Review of Economics and Statistics.
Volume (Year): 83 (2001)
Issue (Month): 4 (November)
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Web page: http://mitpress.mit.edu/journals/
Other versions of this item:
- Whitney Newey, 1999. "Flexible Simulated Moment Estimation of Nonlinear Errors-in-Variables Models," Working papers, Massachusetts Institute of Technology (MIT), Department of Economics 99-02, Massachusetts Institute of Technology (MIT), Department of Economics.
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