Semiparametric Deconvolution with Unknown Error Variance

This file is part of IDEAS, which uses RePEc data

Semiparametric Deconvolution with Unknown Error Variance

Author Info

William C. Horrace () (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020)
Christopher F. Parmeter () (Department of Agricultural and Applied Economics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0401)

Additional information is available for the following registered author(s):

William Clinton Horrace

Abstract

Deconvolution is a useful statistical technique for recovering an unknown density in the presence of measurement error. Typically, the method hinges on stringent assumptions about teh nature of the measurement error, more specifically, that the distribution is *entirely* known. We relax this assumption in the context of a regression error component model and develop an estimator for the unkinown density. We show semi-uniform consistency of the estimator and provide Monte Carlo evidence that demonstrates the merits of the method.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. 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.

Publisher Info
Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 104.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 23 pages
Date of creation: Apr 2008
Date of revision:
Handle: RePEc:max:cprwps:104

Contact details of provider:
Postal: 426 Eggers Hall, Syracuse, New York USA 13244-1020
Phone: (315) 443-3114
Fax: (315) 443-1081
Email:
Web page: http://www-cpr.maxwell.syr.edu
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Martha W. Bonney).

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

This paper has been announced in the following NEP Reports:

NEP-ALL-2008-05-10 (All new papers)
NEP-ECM-2008-05-10 (Econometrics)
NEP-ORE-2008-05-10 (Operations Research)

References listed on IDEAS
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.:

Shunpu Zhang & Rohana Karunamuni, 2000. "Boundary Bias Correction for Nonparametric Deconvolution," Annals of the Institute of Statistical Mathematics, Springer, vol. 52(4), pages 612-629, December. [Downloadable!] (restricted)
Joel L. Horowitz & Marianthi Markatou, 1993. "Semiparametric Estimation Of Regression Models For Panel Data," Econometrics 9309001, EconWPA. [Downloadable!]
Li, Tong & Perrigne, Isabelle & Vuong, Quang, 2000. "Conditionally independent private information in OCS wildcat auctions," Journal of Econometrics, Elsevier, vol. 98(1), pages 129-161, September. [Downloadable!] (restricted)
A. Delaigle & I. Gijbels, 2004. "Bootstrap bandwidth selection in kernel density estimation from a contaminated sample," Annals of the Institute of Statistical Mathematics, Springer, vol. 56(1), pages 19-47, March. [Downloadable!] (restricted)
Elena Krasnokutskaya, 2004. "Identification and Estimation in Highway Procurement Auctions under Unobserved Auction Heterogeneity," PIER Working Paper Archive 05-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania. [Downloadable!]
A. Delaigle & I. Gijbels, 2002. "Estimation of integrated squared density derivatives from a contaminated sample," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 869-886. [Downloadable!] (restricted)
Delaigle, A. & Gijbels, I., 2004. "Practical bandwidth selection in deconvolution kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 249-267, March. [Downloadable!] (restricted)
Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July. [Downloadable!] (restricted)
Fabien Postel-Vinay & Jean-Marc Robin, 2002. "The Distribution of Earnings in an Equilibrium Search Model with State-Dependent Offers and Counteroffers," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(4), pages 989-1016, November. [Downloadable!] (restricted)
Horowitz, Joel L & Markatou, Marianthi, 1996. "Semiparametric Estimation of Regression Models for Panel Data," Review of Economic Studies, Blackwell Publishing, vol. 63(1), pages 145-68, January. [Downloadable!] (restricted)
Delaigle, Aurore & Meister, Alexander, 2007. "Nonparametric Regression Estimation in the Heteroscedastic Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1416-1426, December. [Downloadable!] (restricted)

Full references

Statistics
Access and download statistics

Did you know? All the bibliographic data shown here has been contributed by volunteers, thereby helping to keep this service free.

This page was last updated on 2009-11-27.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.