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Quantile Methods for Stochastic Frontier Analysis

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  • Alecos Papadopoulos
  • Christopher F. Parmeter

Abstract

Quantile regression has become one of the standard tools of econometrics. We examine its compatibility with the special goals of stochastic frontier analysis. We document several conflicts between quantile regression and stochastic frontier analysis. From there we review what has been done up to now, we propose ways to overcome the conflicts that exist, and we develop new tools to do applied efficiency analysis using quantile methods in the context of stochastic frontier models. The work includes an empirical illustration to reify the issues and methods discussed, and catalogs the many open issues and topics for future research.

Suggested Citation

  • Alecos Papadopoulos & Christopher F. Parmeter, 2022. "Quantile Methods for Stochastic Frontier Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 12(1), pages 1-120, November.
    • Handle: RePEc:now:fnteco:0800000042
    DOI: 10.1561/0800000042
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    References listed on IDEAS

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    1. Kaspar Wüthrich, 2020. "A Comparison of Two Quantile Models With Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
    2. 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).
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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. Alecos Papadopoulos & Christopher F. Parmeter, 2024. "The wrong skewness problem in stochastic frontier analysis: a review," Journal of Productivity Analysis, Springer, vol. 61(2), pages 121-134, April.
    3. Carlos Alberto Zúniga-González & Mario Ramoń López & Jorge Luis Icabalceta-Mairena & Elgin Antonio Vivas-Viachica & Napoleon Vicente Blanco-Orozco, 2024. "Study of the Efficiency and Productivity of the Productive Paths of the Bioeconomy in the CNU Universities," World Scientific Book Chapters, in: Sabri Boubaker & Thanh Ngo (ed.), Handbook on Data Envelopment Analysis in Business, Finance, and Sustainability Recent Trends and Developments, chapter 7, pages 221-250, World Scientific Publishing Co. Pte. Ltd..
    4. 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.
    5. Alecos Papadopoulos, 2023. "The noise error component in stochastic frontier analysis," Empirical Economics, Springer, vol. 64(6), pages 2795-2829, June.
    6. Aliya Alimhanova & Andrey Vazhdaev & Artur Mitsel & Anatoly Sidorov, 2022. "Dynamic Model of Enterprise Revenue Management Based on the SFA Model," Mathematics, MDPI, vol. 11(1), pages 1-13, December.
    7. Alecos Papadopoulos, 2024. "Some notes on the asymmetry of the regression error," Journal of Productivity Analysis, Springer, vol. 61(1), pages 37-42, February.

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