stochastic frontier models with random coefficients (replication data)

The paper proposes a stochastic frontier model with random coefficients to separate technical inefficiency from technological differences across firms, and free the frontier model from the restrictive assumption that all firms must share exactly the same technological possibilities. Inference procedures for the new model are developed based on Bayesian techniques, and computations are performed using Gibbs sampling with data augmentation to allow finite-sample inference for underlying parameters and latent efficiencies. An empirical example illustrates the procedure.