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Quantile Stochastic Frontiers

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  • Tsionas, Mike G.

Abstract

In this paper, based on Jradi and Ruggiero (2019). Stochastic Data Envelopment Analysis: A Quantile Regression Approach to Estimate the Production Frontier. European Journal of Operational Research, 278 (2), 385–393] we propose a novel quantile Stochastic Frontier Model (SFM) and develop Markov Chain Monte Carlo techniques for numerical Bayesian inference. In an empirical application to US large banks we document important differences between the Quantile and the traditional SFM, in terms of several aspects of the data. We also document considerable heterogeneity among different quantiles in terms of returns to scale, technical change, efficiency change, technical efficiency, as well as productivity growth.

Suggested Citation

  • Tsionas, Mike G., 2020. "Quantile Stochastic Frontiers," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1177-1184.
  • Handle: RePEc:eee:ejores:v:282:y:2020:i:3:p:1177-1184
    DOI: 10.1016/j.ejor.2019.10.012
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    References listed on IDEAS

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    1. Behr, Andreas, 2010. "Quantile regression for robust bank efficiency score estimation," European Journal of Operational Research, Elsevier, vol. 200(2), pages 568-581, January.
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    5. Wang, Yongqiao & Wang, Shouyang & Dang, Chuangyin & Ge, Wenxiu, 2014. "Nonparametric quantile frontier estimation under shape restriction," European Journal of Operational Research, Elsevier, vol. 232(3), pages 671-678.
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    11. Emir Malikov & Subal C. Kumbhakar & Mike G. Tsionas, 2016. "A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of us Banks in 2001–2010," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1407-1429, November.
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    Cited by:

    1. Tsionas, Mike G. & Assaf, A. George & Andrikopoulos, Athanasios, 2020. "Quantile stochastic frontier models with endogeneity," Economics Letters, Elsevier, vol. 188(C).

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