Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models
This paper establishes that instruments enable the identification of nonparametric regression models in the presence of measurement error by providing a closed form solution for the regression function in terms of Fourier transforms of conditional expectations of observable variables. For parametrically specified regression functions, we propose a root n consistent and asymptotically normal estimator that takes the familiar form of a generalized method of moments estimator with a plugged-in nonparametric kernel density estimate. Both the identification and the estimation methodologies rely on Fourier analysis and on the theory of generalized functions. The finite-sample properties of the estimator are investigated through Monte Carlo simulations. Copyright The Econometric Society 2007.
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Volume (Year): 75 (2007)
Issue (Month): 1 (01)
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- repec:cup:etheor:v:11:y:1995:i:3:p:560-96 is not listed on IDEAS
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