Bayesian analysis of stochastic frontier models

Gary Koop, Mark F.J. Steel

Research output: Chapter in Book/Report/Conference proceedingChapter

40 Citations (Scopus)

Abstract

In this chapter, we described a Bayesian approach to efficiency analysis using stochastic frontier models. With cross-sectional data and a log-linear frontier, a simple Gibbs sampler can be used to carry out Bayesian inference. In the case of a nonlinear frontier, more complicated posterior simulation methods are necessary. Bayesian efficiency measurement with panel data is then discussed. We show how a Bayesian analogue of the classical fixed effects panel data model can be used to calculate the efficiency of each firm relative to the most efficient firm. However, absolute efficiency calculations are precluded in this model and inference on efficiencies can be quite sensitive to prior assumptions. Accordingly, we describe a Bayesian analogue of the classical random effects panel data model which can be used for robust inference on absolute efficiencies. Throughout, we emphasize the computational methods necessary to carry out Bayesian inference. We show how random number generation from well-known distributions is sufficient to develop posterior simulators for a wide variety of models.
Original languageEnglish
Title of host publicationA Companion to Theoretical Econometrics
Pages520-537
Number of pages17
DOIs
Publication statusPublished - 2001

Keywords

  • bayesian analysis
  • stochastic frontier models
  • bayesian inference
  • nonlinear production frontiers
  • panel data

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