Nonparametric tests of tail behavior in stochastic frontier models

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Abstract

This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests for thin-tailed distributional assumptions imposed on these two components. The tests are useful diagnostic tools for stochastic frontier analysis and kernel deconvolution density estimation. A simulation study and applications to four previously studied datasets are provided. In two of these applications, the new tests reject the thin-tailed distributional assumptions such as normal or Laplace, which are commonly imposed in the existing literature.

Original languageEnglish (US)
Pages (from-to)537-562
Number of pages26
JournalJournal of Applied Econometrics
Volume37
Issue number3
DOIs
StatePublished - Apr 1 2022

Keywords

  • deconvolution
  • extreme value theory
  • hypothesis testing
  • inefficiency
  • production

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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