Bayesian analysis of stochastic frontier models

Koop, Gary and Steel, Mark F.J.; (2001) Bayesian analysis of stochastic frontier models. In: A Companion to Theoretical Econometrics. Blackwell, pp. 520-537. ISBN 063121254X (http://dx.doi.org/10.1002/9780470996249.ch25)

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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.

ORCID iDs

Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X and Steel, Mark F.J.;
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