A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data

This paper applies a large number of models to three previously-analyzed data sets, and compares the point estimates and confidence intervals for technical efficiency levels. Classical procedures include multiple comparisons with the best, based on the fixed effects estimates; a univariate version, marginal comparisons with the best; bootstrapping of the fixed effects estimates; and maximum likelihood given a distributional assumption. Bayesian procedures include a Bayesian version of the fixed effects model, and various Bayesian models with informative priors for efficiencies. We find that fixed effects models generally perform poorly; there is a large payoff to distributional assumptions for efficiencies. We do not find much difference between Bayesian and classical procedures, in the sense that the classical MLE based on a distributional assumption for efficiencies gives results that are rather similar to a Bayesian analysis with the corresponding prior.