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)

Abstract: 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
Date: 2005-08
New Economics Papers: this item is included in nep-ecm
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: On Deconvolution as a First Stage Nonparametric Estimator (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:scp:wpaper:05-29

Access Statistics for this paper

More papers in IEPR Working Papers from Institute of Economic Policy Research (IEPR) Contact information at EDIRC.
Bibliographic data for series maintained by ().