Semiparametric Deconvolution with Unknown Error Variance
William C. Horrace
Syracuse University - Department of Economics
Christopher F. Parmeter
Virginia Polytechnic Institute & State University
April 1, 2008
Center for Policy Research Working Paper No. 104
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
Date posted: April 16, 2011
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