|
If you are seeing this message, you may be experiencing temporary network problems. Please wait a few minutes and refresh the page. If the problem persists, you may wish to report it to your local Network Manager.
It is also possible that your web browser is not configured or not able to display style sheets. In this case, although the visual presentation will be degraded, the site should continue to be functional. We recommend using the latest version of Microsoft or Mozilla web browser to help minimise these problems.
Wiley InterScience | |||
  EconometricaVolume 75 Issue 1, Pages 201 - 239 Published Online: 29 Jan 2007 © 2009 The Econometric Society
Abstract | References | Full Text: PDF (Size: 392K) | Related Articles | Citation Tracking  Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models This work was made possible in part through financial support from the National Science Foundation via Grant SES-0452089. The author thanks Jeremy Fox, Ricardo Mayer, Derek Neal, and Xiaohong Chen, as well as participants at seminars given at the Universities of Rochester, Chicago, Maryland, Michigan, UCSD, and UC-Riverside, the 2004 summer meetings of the Econometric Society, and the CIRANO/CIREQ "Operator Methods in Microeconometrics, Time Series and Finance" conference for their helpful comments. Three anonymous referees and a co-editor provided helpful suggestions for a greatly improved presentation. Copyright The Econometric Society 2007 KEYWORDS Errors-in-variables model • Fourier transform • generalized function • semiparametric model ABSTRACTThis 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. Manuscript received December, 2005; final revision received July, 2006. | 
|
