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Markov switching stochastic frontier model

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  • Efthymios G. Tsionas
  • Subal C. Kumbhakar

Abstract

In this paper, we propose a new approach to stochastic frontier models, viz., a Markov switching structure to accommodate cross-sectional parameter heterogeneity and temporal variation in the parameters and technical inefficiency distributions. The Markov Chain Monte Carlo techniques are developed and implemented for Bayesian inferences on parameters and technical efficiency. We illustrate new methods by estimating world production frontiers using international panel data on 59 countries observed for 26 years. Copyright Royal Economic Socciety 2004

Suggested Citation

  • Efthymios G. Tsionas & Subal C. Kumbhakar, 2004. "Markov switching stochastic frontier model," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 398-425, December.
  • Handle: RePEc:ect:emjrnl:v:7:y:2004:i:2:p:398-425
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    Cited by:

    1. Vouldis, Angelos T. & Michaelides, Panayotis G. & Tsionas, Efthymios G., 2010. "Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML," Economic Modelling, Elsevier, vol. 27(3), pages 697-704, May.
    2. Bos, J.W.B. & Economidou, C. & Koetter, M., 2010. "Technology clubs, R&D and growth patterns: Evidence from EU manufacturing," European Economic Review, Elsevier, vol. 54(1), pages 60-79, January.
    3. Yélou, Clément & Larue, Bruno & Tran, Kien C., 2010. "Threshold effects in panel data stochastic frontier models of dairy production in Canada," Economic Modelling, Elsevier, vol. 27(3), pages 641-647, May.
    4. Lu, Zeng-Hua, 2009. "Covariate selection in mixture models with the censored response variable," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2710-2723, May.
    5. Guangjie Li, 2015. "A stochastic frontier model with structural breaks in efficiency and technology," Empirical Economics, Springer, vol. 49(1), pages 131-159, August.
    6. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    7. Hung-pin Lai, 2013. "Estimation of the threshold stochastic frontier model in the presence of an endogenous sample split variable," Journal of Productivity Analysis, Springer, vol. 40(2), pages 227-237, October.
    8. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
    9. Bos, J.W.B. & Economidou, C. & Koetter, M. & Kolari, J.W., 2010. "Do all countries grow alike?," Journal of Development Economics, Elsevier, vol. 91(1), pages 113-127, January.
    10. Drivas, Kyriakos & Economidou, Claire & Tsionas, Efthymios G., 2014. "A Poisson Stochastic Frontier Model with Finite Mixture Structure," MPRA Paper 57485, University Library of Munich, Germany.
    11. Pavlos Almanidis, 2013. "Accounting for heterogeneous technologies in the banking industry: a time-varying stochastic frontier model with threshold effects," Journal of Productivity Analysis, Springer, vol. 39(2), pages 191-205, April.
    12. Drake, Leigh & Hall, Maximilian J.B. & Simper, Richard, 2009. "Bank modelling methodologies: A comparative non-parametric analysis of efficiency in the Japanese banking sector," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(1), pages 1-15, February.
    13. Aivazian, Sergei & Afanasiev, Mikhail & Kudrov, Alexander, 2016. "Clustering methodology of the Russian Federation regions with account of sectoral structure of GRP," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 41, pages 24-46.

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