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 a class of distributions with restricted support, then much weaker smoothness assumptions than hers suffice to ensure [image omitted] consistency of the estimator. In addition, we show that the INLR estimator remains consistent under these weaker smoothness assumptions if the support of the measurement error distribution expands with the sample size. In that case the estimator remains also asymptotically normal with a rate of convergence that is arbitrarily close to [image omitted]. Our results show that deconvolution can be used in a nonparametric first step without imposing restrictive smoothness assumptions on the parametric model.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Econometric Reviews.
Volume (Year): 29 (2010)
Issue (Month): 4 ()
Contact details of provider:
Web page: http://www.tandfonline.com/LECR20
Other versions of this item:
- Yingyao Hu & Geert Ridder, 2005. "On Deconvolution as a First Stage Nonparametric Estimator," IEPR Working Papers 05.29, Institute of Economic Policy Research (IEPR).
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Susanne M. Schennach, 2004. "Estimation of Nonlinear Models with Measurement Error," Econometrica, Econometric Society, vol. 72(1), pages 33-75, 01.
- Li, Tong, 2002. "Robust and consistent estimation of nonlinear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 110(1), pages 1-26, September.
- Horowitz, Joel L & Markatou, Marianthi, 1996. "Semiparametric Estimation of Regression Models for Panel Data," Review of Economic Studies, Wiley Blackwell, vol. 63(1), pages 145-68, January.
- Newey, W.K., 1991.
"The Asymptotic Variance of Semiparametric Estimators,"
583, Massachusetts Institute of Technology (MIT), Department of Economics.
- Newey, Whitney K, 1994. "The Asymptotic Variance of Semiparametric Estimators," Econometrica, Econometric Society, vol. 62(6), pages 1349-82, November.
- Newey, W.K., 1989. "The Asymptotic Variance Of Semiparametric Estimotors," Papers 346, Princeton, Department of Economics - Econometric Research Program.
- Xiaohong Chen & Han Hong & Elie Tamer, 2005. "Measurement Error Models with Auxiliary Data," Review of Economic Studies, Oxford University Press, vol. 72(2), pages 343-366.
- Li, Tong & Vuong, Quang, 1998. "Nonparametric Estimation of the Measurement Error Model Using Multiple Indicators," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 139-165, May.
- Susanne Schennach, 2013. "Convolution without independence," CeMMAP working papers CWP46/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- An, Yonghong & Hu, Yingyao, 2012.
"Well-posedness of measurement error models for self-reported data,"
Journal of Econometrics,
Elsevier, vol. 168(2), pages 259-269.
- Yonghong An & Yingyao Hu, 2009. "Well-Posedness of Measurement Error Models for Self-Reported Data," Economics Working Paper Archive 556, The Johns Hopkins University,Department of Economics.
- Yonghong An & Yingyao Hu, 2009. "Well-posedness of measurement error models for self-reported data," CeMMAP working papers CWP35/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.