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

This paper appliesa large number of models to three previously-analyzed data sets,and compares the point estimates and confidence intervals fortechnical efficiency levels. Classical procedures include multiplecomparisons with the best, based on the fixed effects estimates;a univariate version, marginal comparisons with the best; bootstrappingof the fixed effects estimates; and maximum likelihood givena distributional assumption. Bayesian procedures include a Bayesianversion of the fixed effects model, and various Bayesian modelswith informative priors for efficiencies. We find that fixedeffects models generally perform poorly; there is a large payoffto distributional assumptions for efficiencies. We do not findmuch difference between Bayesian and classical procedures, inthe sense that the classical MLE based on a distributional assumptionfor efficiencies gives results that are rather similar to a Bayesiananalysis with the corresponding prior.     

Trade,Investment and Economic Interdependence between South Korea and China*

The Korean economy has been significantly affected by the emergence of China. It is now the largest market for Korean exports and a major supplier of its low‐cost imports but has at the same time become a serious challenger to Korea in the world markets for manufacturing exports. This paper investigates changes in China's export structure and its effect on Korea, and bilateral trade between the two. It also examines the motives for Korean investment in China and its effect on bilateral trade and cross‐border production networks.     

On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data

We study the construction of confidence intervals for efficiency levels of individual firms in stochastic frontier models with panel data. The focus is on bootstrapping and related methods. We start with a survey of various versions of the bootstrap. We also propose a simple parametric alternative in which one acts as if the␣identity of the best firm is known. Monte Carlo simulations indicate that the parametric method works better than the␣percentile bootstrap, but not as well as bootstrap methods that make bias corrections. All of these methods are valid␣only for large time-series sample size (T), and correspondingly none of the methods yields very accurate confidence intervals except when T is large enough that the identity of the best firm is clear. We also present empirical results for two well-known data sets.