Density deconvolution with Laplace errors and unknown variance

Jun Cai, William C. Horrace, Christopher F. Parmeter

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)103-113
Number of pages11
JournalJournal of Productivity Analysis
Issue number2-3
StatePublished - Dec 2021


  • Ordinary smooth
  • Semi-parametric
  • Stochastic frontier

ASJC Scopus subject areas

  • Business and International Management
  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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