Empirical Economics

, Volume 56, Issue 2, pp 399–422 | Cite as

Bayesian inference in threshold stochastic frontier models

  • Efthymios G. Tsionas
  • Kien C. TranEmail author
  • Panayotis G. Michaelides


In this paper, we generalize the stochastic frontier model to allow for heterogeneous technologies and inefficiencies in a structured way that allows for learning and adapting. We propose a general model and various special cases, organized around the idea that there is switching or transition from one technology to the other(s), and construct threshold stochastic frontier models. We suggest Bayesian inferences for the general model proposed here and its special cases using Gibbs sampling with data augmentation. The new techniques are applied, with very satisfactory results, to a panel of world production functions using, as switching or transition variables, human capital, age of capital stock (representing input quality), as well as a time trend to capture structural switching.


Stochastic frontier Regime switching Efficiency measurement Bayesian inference Markov Chain Monte Carlo 

JEL Classification

C11 C13 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Efthymios G. Tsionas
    • 1
    • 2
  • Kien C. Tran
    • 3
    Email author
  • Panayotis G. Michaelides
    • 4
    • 5
  1. 1.Lancaster University Management SchoolLancasterUK
  2. 2.Athens University of Economics and BusinessAthensGreece
  3. 3.Department of EconomicsUniversity of LethbridgeLethbridgeCanada
  4. 4.Systemic Risk CentreLondon School of EconomicsLondonUK
  5. 5.School of Applied Mathematics and PhysicsNational Technical University of AthensAthensGreece

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