Estimation of inefficiency in stochastic frontier models: a Bayesian kernel approach

Guohua Feng, Chuan Wang, Xibin Zhang

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


We propose a kernel-based Bayesian framework for the analysis of stochastic frontiers and efficiency measurement. The primary feature of this framework is that the unknown distribution of inefficiency is approximated by a transformed Rosenblatt-Parzen kernel density estimator. To justify the kernel-based model, we conduct a Monte Carlo study and also apply the model to a panel of U.S. large banks. Simulation results show that the kernel-based model is capable of providing more precise estimation and prediction results than the commonly-used exponential stochastic frontier model. The Bayes factor also favors the kernel-based model over the exponential model in the empirical application.

Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalJournal of Productivity Analysis
Issue number1
Publication statusPublished - 15 Feb 2019


  • Efficiency measurement
  • Kernel density estimation
  • Markov Chain Monte Carlo
  • Stochastic distance frontier

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