Bayesian inference in threshold stochastic frontier models

In this paper, we generalize the stochastic frontier model to allow for heterogeneous technologies and inefficiencies in a structured way that allows for learning and adapting. We propose a general model and various special cases, organized around the idea that there is switching or transition from one technology to the other(s), and construct threshold stochastic frontier models. We suggest Bayesian inferences for the general model proposed here and its special cases using Gibbs sampling with data augmentation. The new techniques are applied, with very satisfactory results, to a panel of world production functions using, as switching or transition variables, human capital, age of capital stock (representing input quality), as well as a time trend to capture structural switching