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Quantile Methods for Stochastic Frontier Analysis

Author

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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, 2023. "The noise error component in stochastic frontier analysis," Empirical Economics, Springer, vol. 64(6), pages 2795-2829, June.
    3. 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.
    4. 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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