Working Paper

Abstract

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