Wrong skewness and finite sample correction in the normal-half normal stochastic frontier model

Jun Cai, Qu Feng, William C. Horrace, Guiying Laura Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In parametric stochastic frontier models, the composed error is specified as the sum of a two-sided noise component and a one-sided inefficiency component, which is usually assumed to be half-normal, implying that the error distribution is skewed in one direction. In practice, however, estimation residuals may display skewness in the wrong direction. Model respecification or pulling a new sample is often prescribed. Since wrong skewness may manifest as a finite sample problem, this paper proposes a finite sample adjustment to existing estimators to obtain the desired direction of residual skewness. This provides an alternative empirical approach to deal with the wrong skewness problem that does not require respecification of the model.

Original languageEnglish (US)
Pages (from-to)2837-2866
Number of pages30
JournalEmpirical Economics
Volume60
Issue number6
DOIs
StatePublished - Jun 2021

Keywords

  • BIC
  • Constrained estimators
  • MLE
  • Skewness
  • Stochastic frontier model

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics (miscellaneous)
  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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