Semiparametric deconvolution with unknown error variance

William C. Horrace, Christopher F. Parmeter

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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 an application to the stochastic frontier model.

Original languageEnglish (US)
Pages (from-to)129-141
Number of pages13
JournalJournal of Productivity Analysis
Issue number2
StatePublished - Apr 2011


  • Error component
  • Ordinary smooth
  • Semi-uniform consistency

ASJC Scopus subject areas

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


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