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Semiparametric Deconvolution with Unknown Error Variance

Center for Policy Research Working Paper No. 104

23 Pages Posted: 16 Apr 2011  

William C. Horrace

Syracuse University - Department of Economics

Christopher F. Parmeter

Virginia Polytechnic Institute & State University

Date Written: April 1, 2008

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.

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

JEL Classification: C14, C21

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: https://ssrn.com/abstract=1808987 or http://dx.doi.org/10.2139/ssrn.1808987

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

Christopher F. Parmeter

Virginia Polytechnic Institute & State University ( email )

Blacksburg, VA 24061
United States

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