  Contribution of this paper lies in the formulation and estimation of a
generalized model for stochastic frontier analysis (SFA) that nests virtually
all forms used and includes some that have not been considered so far. The
model is based on the generalized t distribution for the observation error and
the generalized beta distribution of the second kind for the
inefficiency-related term. We use this general error structure framework for
formal testing, to compare alternative specifications and to conduct model
averaging. This allows us to deal with model specification uncertainty, which
is one of the main unresolved issues in SFA, and to relax a number of
potentially restrictive assumptions embedded within existing SF models. We also
develop Bayesian inference methods that are less restrictive compared to the
ones used so far and demonstrate feasible approximate alternatives based on
maximum likelihood.
