Nonparametric estimation of the determinants of inefficiency

Christopher Parmeter, Hung Jen Wang, Subal C. Kumbhakar

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

We consider the benchmark stochastic frontier model where inefficiency is directly influenced by observable determinants. In this setting, we estimate the stochastic frontier and the conditional mean of inefficiency without imposing any distributional assumptions. To do so we cast this model in the partly linear regression framework for the conditional mean. We provide a test of correct parametric specification of the scaling function. An empirical example is also provided to illustrate the practical value of the methods described here.

Original languageEnglish (US)
Pages (from-to)205-221
Number of pages17
JournalJournal of Productivity Analysis
Volume47
Issue number3
DOIs
StatePublished - Jun 1 2017

Keywords

  • Bandwidth
  • Heteroskedasticity
  • Kernel
  • Partly linear

ASJC Scopus subject areas

  • Business and International Management
  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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