Semiparametric Deconvolution with Unknown Error Variance
AbstractDeconvolution 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 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.
Bibliographic InfoPaper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 104.
Length: 23 pages
Date of creation: Apr 2008
Date of revision:
Contact details of provider:
Postal: 426 Eggers Hall, Syracuse, New York USA 13244-1020
Phone: (315) 443-3114
Fax: (315) 443-1081
Web page: http://www.maxwell.syr.edu/cpr.aspx
More information through EDIRC
Error component; ordinary smooth; semi-uniform consistency;
Other versions of this item:
- William Horrace & Christopher Parmeter, 2011. "Semiparametric deconvolution with unknown error variance," Journal of Productivity Analysis, Springer, vol. 35(2), pages 129-141, April.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- 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)
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.:
- 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.
- Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
- 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.
- 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.
- 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.
- 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.
- 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.
- Joel L. Horowitz & Marianthi Markatou, 1993. "Semiparametric Estimation Of Regression Models For Panel Data," Econometrics 9309001, EconWPA.
- 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.
- Wang, Wei Siang & Schmidt, Peter, 2009.
"On the distribution of estimated technical efficiency in stochastic frontier models,"
Journal of Econometrics,
Elsevier, vol. 148(1), pages 36-45, January.
- Wei Siang Wang & Peter Schmidt, 2007. "On The Distribution of Estimated Technical Efficiency in Stochastic Frontier Models," CEPA Working Papers Series WP022007, School of Economics, University of Queensland, Australia.
- 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.
- Raymond J. Carroll & Peter Hall, 2004. "Low order approximations in deconvolution and regression with errors in variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 31-46.
- Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
- 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.
- 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.
- Peter Hall & Peihua Qiu, 2005. "Discrete-transform approach to deconvolution problems," Biometrika, Biometrika Trust, vol. 92(1), pages 135-148, March.
- Wei Wang & Christine Amsler & Peter Schmidt, 2011. "Goodness of fit tests in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 35(2), pages 95-118, April.
- repec:max:cprwps:154 is not listed on IDEAS
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Kelly Bogart) or (Katrina Wingle).
If references are entirely missing, you can add them using this form.