Bayesian estimation for semiparametric stochastic frontier models

This thesis investigates three topics in stochastic frontier models using panel data via Bayesian simulations. First, the marginal posterior density of inefficiencies is approximated by a conditional density. The simulation result shows that the predictive inefficiency distribution derived from this model can capture information about inefficiencies very well. Further, we propose a data-driven adaptive bandwidth kernel estimator for the unknown frontier function. The application and simulation results imply that the adaptive bandwidth model outperforms the fixed bandwidth model. Finally, we present two sampling algorithms to model the unobserved grouped heterogeneity in the stochastic frontier mode with a latent class structure. Using the algorithms, we find evidence supporting the grouped heterogeneity in the cost frontier function.