Abstract

http://ssrn.com/abstract=1808987
 
 

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Semiparametric Deconvolution with Unknown Error Variance


William C. Horrace


Syracuse University - Department of Economics; National Bureau of Economic Research (NBER)

Christopher F. Parmeter


Virginia Polytechnic Institute & State University

April 1, 2008

Center for Policy Research Working Paper No. 104

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 the 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 unknown density. We show semi-uniform consistency of the estimator and provide Monte Carlo evidence that demonstrates the merits of the method.

Number of Pages in PDF File: 23

Keywords: Error Component, Ordinary Smooth, Semi-Uniform Consistency

JEL Classification: C14, C21

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Date posted: April 16, 2011  

Suggested Citation

Horrace, William C. and Parmeter, Christopher F., Semiparametric Deconvolution with Unknown Error Variance (April 1, 2008). Center for Policy Research Working Paper No. 104. Available at SSRN: http://ssrn.com/abstract=1808987 or http://dx.doi.org/10.2139/ssrn.1808987

Contact Information

William C. Horrace (Contact Author)
Syracuse University - Department of Economics ( email )
Syracuse, NY 13244-1020
United States
315-443-9061 (Phone)
315-443-1081 (Fax)
HOME PAGE: http://faculty.maxwell.syr.edu/whorrace
National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Christopher F. Parmeter
Virginia Polytechnic Institute & State University ( email )
Blacksburg, VA 24061
United States
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