Penalized sieve estimation of zero-inefficiency stochastic frontiers

Jun Cai, William C. Horrace, Christopher F. Parmeter

Research output: Contribution to journalArticlepeer-review


Stochastic frontier models for cross-sectional data typically assume that the one-sided distribution of firm-level inefficiency is continuous. However, it may be reasonable to hypothesize that inefficiency is continuous except for a discrete mass at zero capturing fully efficient firms (zero-inefficiency). We propose a sieve-type density estimator for such a mixture distribution in a nonparametric stochastic frontier setting under a unimodality-at-zero assumption. Consistency, rates of convergence and asymptotic normality of the estimators are established, as well as a test of the zero-inefficiency hypothesis. Simulations and two applications are provided to demonstrate the practicality of the method.

Original languageEnglish (US)
Pages (from-to)41-65
Number of pages25
JournalJournal of Applied Econometrics
Issue number1
StatePublished - Jan 1 2024

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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