Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models
证明工具变量能识别存在测量误差的非参数回归模型，给出回归函数的闭式解，并提出根n一致渐近正态的广义矩估计量，通过蒙特卡洛模拟考察有限样本性质。
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.