Full likelihood inference in normal-gamma stochastic frontier models

The paper takes up inference in the stochastic frontier model with gamma di stributed inefficiency terms, without restricting the gamma distribution to known integer values of its shape parameter (the Erlang form). The paper s hows that Gibbs sampling with data augmentation can be used in a computatio nally efficient way to explore the posterior distribution of the model and conduct inference regarding parameters as well as functions of interest rel ated to technical inefficiency.