Instrumental variable estimation of nonlinear errors-in-variables models

• 成果类型: Article
• 署名作者: Schennach, Susanne M.
• 署名单位: University of Chicago
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/j.1468-0262.2007.00736.x
• 发表日期: 2007
• 页码: 201-239
• 关键词: semiparametric estimation; Nonparametric Regression; CONSISTENT ESTIMATION; identification; CONVERGENCE; rates

摘要

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.