Nonparametric efficiency estimation in stochastic environments

Thierry Post, Laurens Cherchye, Timo Kuosmanen

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


This paper develops a new nonparametric model for efficiency estimation. In contrast to Data Envelopment Analysis (DEA), it does not impose debatable production assumptions like free disposability and convexity, and it does not assume that the data are measured without error. The estimators are asymptotically unbiased and have an asymptotic variance that is comparable to that of stochastic frontier estimators (provided the latter use a correct specification of the functional form for the production relationships). In addition, the estimators can be computed using a simple enumeration algorithm.

Original languageEnglish
Pages (from-to)645-655
Number of pages11
JournalOperations Research
Issue number4
Publication statusPublished - 2003

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research


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