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Alternative technical efficiency measures: Skew, bias and scale

Author

Listed:
  • Qu Feng
  • William C. Horrace

Abstract

In the fixed-effects stochastic frontier model an efficiency measure relative to the best firm in the sample is universally employed. This paper considers a new measure relative to the worst firm in the sample. We find that estimates of this measure have smaller bias than those of the traditional measure when the sample consists of many firms near the efficient frontier. Moreover, a two-sided measure relative to both the best and the worst firms is proposed. Simulations suggest that the new measures may be preferred depending on the skewness of the inefficiency distribution and the scale of efficiency differences.
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Suggested Citation

  • Qu Feng & William C. Horrace, 2012. "Alternative technical efficiency measures: Skew, bias and scale," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 253-268, March.
    • Handle: RePEc:wly:japmet:v:27:y:2012:i:2:p:253-268
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    Cited by:

    1. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
    2. William Horrace & Seth Richards-Shubik & Ian Wright, 2015. "Expected efficiency ranks from parametric stochastic frontier models," Empirical Economics, Springer, vol. 48(2), pages 829-848, March.
    3. Young Hoon Lee & Hayley Jang & Sun Ho Hwang, 2015. "Market Competition and Threshold Efficiency in the Sports Industry," Journal of Sports Economics, , vol. 16(8), pages 853-870, December.
    4. Pawlak, Tomasz P. & Litwiniuk, Bartosz, 2021. "Ellipsoidal one-class constraint acquisition for quadratically constrained programming," European Journal of Operational Research, Elsevier, vol. 293(1), pages 36-49.
    5. Elisa Fusco & Giuseppe Arbia & Francesco Vidoli & Vincenzo Nardelli, 2024. "On Spatio-Temporal Stochastic Frontier Models," Papers 2410.20915, arXiv.org.
    6. Feng, Qu & Horrace, William C., 2012. "Estimating technical efficiency in micro panels," Economics Letters, Elsevier, vol. 117(3), pages 730-733.
    7. Sungwon Lee & Young Lee, 2014. "Stochastic frontier models with threshold efficiency," Journal of Productivity Analysis, Springer, vol. 42(1), pages 45-54, August.
    8. Tomer Blumkin & Leif Danziger & Eran Yashiv, 2017. "Optimal unemployment benefit policy and the firm productivity distribution," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(1), pages 36-59, February.
    9. Zhang, Hongsong, "undated". "Biased Technology and Contribution of Technological Change to Economic Growth: Firm-Level Evidence," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150225, Agricultural and Applied Economics Association.
    10. Kutlu, Levent, 2017. "A constrained state space approach for estimating firm efficiency," Economics Letters, Elsevier, vol. 152(C), pages 54-56.
    11. Qu Feng & William Horrace & Guiying Laura Wu, 2013. "Wrong Skewness and Finite Sample Correction in Parametric Stochastic Frontier Models Abstract: In parametric stochastic frontier models, the composed error is specified as the sum of a two-sided noise," Center for Policy Research Working Papers 154, Center for Policy Research, Maxwell School, Syracuse University.
    12. William C. Horrace & Christopher F. Parmeter, 2018. "A Laplace stochastic frontier model," Econometric Reviews, Taylor & Francis Journals, vol. 37(3), pages 260-280, March.
    13. Feng, Qu & Wu, Guiying Laura & Yuan, Mengying & Zhou, Shihao, 2022. "Save lives or save livelihoods? A cross-country analysis of COVID-19 pandemic and economic growth," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 221-256.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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