On the Use of Panel Data in Bayesian Stochastic Frontier Models

We consider a Bayesian analysis of the stochastic frontier model with composed error.Under a commonly used class of (partly) noninformative prior distributions, the existence of the posterior distribution and of posterior moments is examined.Viewing this model as a Normal linear regression model with regression parameters corresponding to both the frontier and the inefficiency terms, generates the insights used to derive results in a very wide framework.It is found that in pure cross-section models posterior inference is precluded under this ``usual'' class of priors.Existence of a well-defined posterior distribution crucially hinges upon the structure imposed on the inefficiency terms.Exploiting panel data naturally suggests the use of more structured models, where Bayesian inference can be conducted.