Bayesian Inference and Gibbs Sampling in Generalized True Random-Effects Models

Abstract

The paper investigates Bayesian approach to estimate generalized true
random-effects models (GTRE). The analysis shows that under suitably defined
priors for transient and persistent inefficiency terms the posterior characteristics
of such models are well approximated using simple Gibbs sampling. No model
re-parameterization is required. The proposed modification not only allows
us to make more reasonable (less informative) assumptions as regards prior
transient and persistent inefficiency distribution but also appears to be more
reliable in handling especially noisy datasets. Empirical application furthers
the research into stochastic frontier analysis using GTRE models by examining
the relationship between inefficiency terms in GTRE, true random-effects,
generalized stochastic frontier and a standard stochastic frontier model.