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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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    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).
    2. M.V. Leonov, 2021. "Review of Modern Approaches for Assessing the Effectiveness of Banking," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 20(2), pages 294-326.
    3. Michael L. Polemis & Mike G. Tsionas, 2023. "The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1602-1621, April.
    4. Alecos Papadopoulos, 2023. "The noise error component in stochastic frontier analysis," Empirical Economics, Springer, vol. 64(6), pages 2795-2829, June.
    5. Zhao, Shirong, 2021. "Quantile estimation of stochastic frontier models with the normal–half normal specification: A cumulative distribution function approach," Economics Letters, Elsevier, vol. 206(C).
    6. E. Fusco & R. Benedetti & F. Vidoli, 2023. "Stochastic frontier estimation through parametric modelling of quantile regression coefficients," Empirical Economics, Springer, vol. 64(2), pages 869-896, February.
    7. Jradi, Samah & Parmeter, Christopher F. & Ruggiero, John, 2021. "Quantile estimation of stochastic frontiers with the normal-exponential specification," European Journal of Operational Research, Elsevier, vol. 295(2), pages 475-483.
    8. Stead, Alexander D. & Wheat, Phill & Greene, William H., 2023. "Robust maximum likelihood estimation of stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 309(1), pages 188-201.
    9. Monje, Juan Cabas & Sidhoum, Amer Ait & Gil, Jose M., 2021. "Investigating Technical Efficiency of Spanish Pig Farming: A Quantile Regression Approach," 2021 Conference, August 17-31, 2021, Virtual 315196, International Association of Agricultural Economists.

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