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Bayesian inference and Gibbs sampling in generalized true random-effects model

Makieła, Kamil (2016): Bayesian inference and Gibbs sampling in generalized true random-effects model.

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Abstract

The paper investigates Bayesian approach to estimating generalized true random-effects model (GTRE). Results show that under suitably defined priors for transient and persistent inefficiency terms the posterior characteristics of the model are well approximated using simple Gibbs sampling. Model reparametrization is unnecessary and it leads to much more time-consuming simulations. The new model also allows us to make more reasonable assumptions about prior efficiency distribution and appears more reliable in handling noisy datasets. Empirical application furthers the research into stochastic frontier analysis using GTRE by examining its relationship with other models known in the literature.

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