Density Deconvolution with Laplace Errors and Unknown Variance

Author(s)/Creator(s)

Jun Cai, Syracuse UniversityFollow
William C. Horrace, Syracuse UniversityFollow
christopher parmeter, University of MiamiFollow

Description/Abstract

We consider density deconvolution with zero-mean Laplace noise in the context of an error component regression model. We adapt the minimax deconvolution methods of Meister (2006) to allow estimation of the unknown noise variance. We propose a semi-uniformly consistent estimator for an ordinary-smooth target density and a modified “variance truncation device" for the unknown noise variance. We provide a simulation study and practical guidance for the choice of smoothness parameters of the ordinary-smooth target density. We apply restricted versions of our estimator to a stochastic frontier model of US banks and to a measurement error model of daily saturated fat intake.

Document Type

Working Paper

Date

6-2020

Keywords

Efficiency Estimation, Laplace Distribution, Stochastic Frontier

Language

English

Series

Working Papers Series

Disciplines

Economic Policy | Economics | Public Affairs, Public Policy and Public Administration

ISSN

1525-3066

Additional Information

Working paper no. 225

Revised from March 2020

Recommended Citation

Cai, Jun; Horrace, William C.; and parmeter, christopher, "Density Deconvolution with Laplace Errors and Unknown Variance" (2020). Center for Policy Research. 257.
https://surface.syr.edu/cpr/257

Source

Local input

Creative Commons License

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