Stationary Points for Parametric Stochastic Frontier Models

William C Horrace, Ian A. Wright

Research output: Contribution to journalArticle

Abstract

Stationary point results on the normal–half-normal stochastic frontier model are generalized using the theory of the Dirac delta, and distribution-free conditions are established to ensure a stationary point in the likelihood as the variance of the inefficiency distribution goes to zero. Stability of the stationary point and “wrong skew” results are derived or simulated for common parametric assumptions on the model. We discuss identification and extensions to more general stochastic frontier models.

Original languageEnglish (US)
JournalJournal of Business and Economic Statistics
DOIs
StatePublished - Jan 1 2019

Fingerprint

Stochastic Frontier
Stationary point
Distribution-free
Skew
Paul Adrien Maurice Dirac
Likelihood
Model
Zero
Stochastic frontier model

Keywords

  • Dirac delta
  • Generalized function
  • Inefficiency estimation
  • Ordinary least squares
  • Singular distribution

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Cite this

Stationary Points for Parametric Stochastic Frontier Models. / Horrace, William C; Wright, Ian A.

In: Journal of Business and Economic Statistics, 01.01.2019.

Research output: Contribution to journalArticle

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