Semiparametric Deconvolution with Unknown Error Variance

Author(s)/Creator(s)

William C. Horrace, Syracuse University. Center for Policy ResearchFollow
Christopher F. Parmeter, Virginia Polytechnic Institute and State University, Department of Agriculural and Applied EconomicsFollow

Description/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.

Document Type

Working Paper

Date

2008

Keywords

semi-uniform consistency, error component, semiparametric deconvolution

Language

English

Series

Working Papers Series

Disciplines

Mathematics

Additional Information

Working paper no. 104

Harvest from RePEc at http://repec.org

Recommended Citation

Horrace, William C. and Parmeter, Christopher F., "Semiparametric Deconvolution with Unknown Error Variance" (2008). Center for Policy Research. 62.
https://surface.syr.edu/cpr/62

Source

Metadata from RePEc

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.