On Deconvolution as a First Stage Nonparametric Estimator
Yingyao Hu and
Geert Ridder ()
No 05.29, IEPR Working Papers from Institute of Economic Policy Research (IEPR)
We 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.
Pages: 30 pages
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Journal Article: On Deconvolution as a First Stage Nonparametric Estimator (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:scp:wpaper:05-29
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