Center for Policy Research Working Paper No. 104
23 Pages Posted: 16 Apr 2011
Date Written: April 1, 2008
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: 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