On Deconvolution as a First Stage Nonparametric Estimator
AbstractWe reconsider Taupin’s (2001) Integrated Nonlinear Regression (INLR) estimator for a nonlinear regression with a mismeasured covariate. We find that if we restrict the distribution of the measurement error to the class of range-restricted distributions, then weak smoothness assumptions suffice to ensure sqrt(n) consistency of the estimator. The restriction to such distributions is innocuous, because it does not affect the fit to the data. Our results show that deconvolution can be used in a nonparametric first step without imposing restrictive smoothness assumptions on the parametric model.
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Bibliographic InfoPaper provided by Institute of Economic Policy Research (IEPR) in its series IEPR Working Papers with number 05.29.
Length: 30 pages
Date of creation: Aug 2005
Date of revision:
Other versions of this item:
- Yingyao Hu & Geert Ridder, 2010. "On Deconvolution as a First Stage Nonparametric Estimator," Econometric Reviews, Taylor & Francis Journals, vol. 29(4), pages 365-396.
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