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Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier

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  • Jradi, Samah
  • Ruggiero, John

Abstract

Data Envelopment Analysis was developed as a deterministic model that assumed that deviations from the production frontier were one sided representing technical inefficiency. The model provides biased estimates of production and inefficiency if deviations from the frontier arise not only from inefficiency but also from statistical noise. Banker (1988, “Stochastic Data Envelopment Analysis,” Working Paper, Carnegie Mellon University) extended Data Envelopment Analysis with a stochastic model to allow not only inefficiency but also statistical noise. Banker's model can be considered a nonparametric quantile regression. Using the celebrated Afriat constraints, the model estimates a piecewise linear production function through the middle of the data. In this paper, we extend Banker's Stochastic DEA model by considering a semi-parametric model that identifies the most likely quantile based on assumptions of the composed error terms. We focus on the most common stochastic frontier model with an error structured constrained to a convolution of the normal and half-normal distributions. Using simulated data, we compare the model to the econometric stochastic frontier model under different distributional assumptions.

Suggested Citation

  • Jradi, Samah & Ruggiero, John, 2019. "Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier," European Journal of Operational Research, Elsevier, vol. 278(2), pages 385-393.
  • Handle: RePEc:eee:ejores:v:278:y:2019:i:2:p:385-393
    DOI: 10.1016/j.ejor.2018.11.017
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    Cited by:

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    3. 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.
    4. Tsionas, Mike G., 2020. "Quantile Stochastic Frontiers," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1177-1184.
    5. Khodadadipour, M. & Hadi-Vencheh, A. & Behzadi, M.H. & Rostamy-malkhalifeh, M., 2021. "Undesirable factors in stochastic DEA cross-efficiency evaluation: An application to thermal power plant energy efficiency," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 613-628.
    6. Olesen, O.B. & Ruggiero, J., 2022. "The hinging hyperplanes: An alternative nonparametric representation of a production function," European Journal of Operational Research, Elsevier, vol. 296(1), pages 254-266.
    7. Tsionas, Mike G. & Assaf, A. George & Andrikopoulos, Athanasios, 2020. "Quantile stochastic frontier models with endogeneity," Economics Letters, Elsevier, vol. 188(C).
    8. 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).
    9. Jinpei Liu & Mengdi Fang & Feifei Jin & Chengsong Wu & Huayou Chen, 2020. "Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks’ Sustainable Development," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
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    11. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Generalized quantile and expectile properties for shape constrained nonparametric estimation," European Journal of Operational Research, Elsevier, vol. 310(2), pages 914-927.
    12. Ioannis E. Tsolas, 2020. "Benchmarking Wind Farm Projects by Means of Series Two-Stage DEA," Clean Technol., MDPI, vol. 2(3), pages 1-12, September.
    13. 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.
    14. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    15. Ghimire, Sarad & Amin, Saman Hassanzadeh & Wardley, Leslie J., 2021. "Developing new data envelopment analysis models to evaluate the efficiency in Ontario Universities," Journal of Informetrics, Elsevier, vol. 15(3).
    16. Zhang, Ning & Huang, Xuhui & Liu, Yunxiao, 2021. "The cost of low-carbon transition for China's coal-fired power plants: A quantile frontier approach," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    17. 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.
    18. 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.

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