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Quantile estimation of the stochastic frontier model

Author

Listed:
  • Jradi, Samah
  • Parmeter, Christopher F.
  • Ruggiero, John

Abstract

The stochastic frontier model remains popular within the field of efficiency analysis and yet it remains deeply connected to the notion of a conditional mean. Recent research has attempted to conceive of, and estimate, the stochastic frontier model in a quantile setting. We demonstrate here that the stochastic frontier corresponds explicitly to a specific quantile of the output distribution and provide a computational approach to recover this quantile. An empirical illustration demonstrates comparable performance with more classical methods of estimation of the stochastic frontier model.

Suggested Citation

  • Jradi, Samah & Parmeter, Christopher F. & Ruggiero, John, 2019. "Quantile estimation of the stochastic frontier model," Economics Letters, Elsevier, vol. 182(C), pages 15-18.
    • Handle: RePEc:eee:ecolet:v:182:y:2019:i:c:p:15-18
    DOI: 10.1016/j.econlet.2019.05.038
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    Citations

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    Cited by:

    1. Bernstein, David H. & Parmeter, Christopher F. & Tsionas, Mike G., 2023. "On the performance of the United States nuclear power sector: A Bayesian approach," Energy Economics, Elsevier, vol. 125(C).
    2. Zangin Zeebari & Kristofer Månsson & Pär Sjölander & Magnus Söderberg, 2023. "Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market," Journal of Productivity Analysis, Springer, vol. 59(1), pages 79-97, February.
    3. William C. Horrace & Christopher F. Parmeter & Ian A. Wright, 2024. "On asymmetry and quantile estimation of the stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 61(1), pages 19-36, February.
    4. Tsionas, Mike G., 2020. "Quantile Stochastic Frontiers," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1177-1184.
    5. Shirong Zhao & Jeremy Losak, 2024. "Two-tiered stochastic frontier models: a Bayesian perspective," Journal of Productivity Analysis, Springer, vol. 61(2), pages 85-106, April.
    6. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Non-crossing convex quantile regression," Economics Letters, Elsevier, vol. 233(C).
    7. 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.
    8. Tsionas, Mike G. & Assaf, A. George & Andrikopoulos, Athanasios, 2020. "Quantile stochastic frontier models with endogeneity," Economics Letters, Elsevier, vol. 188(C).
    9. 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).
    10. Juan Cabas Monje & Bouali Guesmi & Amer Ait Sidhoum & José María Gil, 2023. "Measuring technical efficiency of Spanish pig farming: Quantile stochastic frontier approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(4), pages 688-703, October.
    11. Christopher F. Parmeter & Shirong Zhao, 2023. "An alternative corrected ordinary least squares estimator for the stochastic frontier model," Empirical Economics, Springer, vol. 64(6), pages 2831-2857, June.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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.

    More about this item

    Keywords

    Quantile function; True quantile; Skewed normal; Efficiency;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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