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Alternative technical efficiency measures: Skew, bias and scale

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  • 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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    1. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    2. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    3. Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 77-105.
    4. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    5. Hall, Peter & Hardle, Wolfgang & Simar, Leopold, 1993. "On the inconsistency of bootstrap distribution estimators," Computational Statistics & Data Analysis, Elsevier, vol. 16(1), pages 11-18, June.
    6. Myungsup Kim & Yangseon Kim & Peter Schmidt, 2007. "On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data," Journal of Productivity Analysis, Springer, vol. 28(3), pages 165-181, December.
    7. William C. Horrace & Peter Schmidt, 2000. "Multiple comparisons with the best, with economic applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 1-26.
    8. Feng, Qu & Horrace, William C., 2007. "Fixed-effect estimation of technical efficiency with time-invariant dummies," Economics Letters, Elsevier, vol. 95(2), pages 247-252, May.
    9. Simar, L., 1991. "Estimating efficiencies from frontier models with panel data: a comparison of parametric, non-parametric and semi-parametric methods with boot strapping," CORE Discussion Papers 1991026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, University Library of Munich, Germany.
    11. Entani, Tomoe & Maeda, Yutaka & Tanaka, Hideo, 2002. "Dual models of interval DEA and its extension to interval data," European Journal of Operational Research, Elsevier, vol. 136(1), pages 32-45, January.
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    Citations

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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. Qu Feng & William Horrace & Guiying Laura Wu, 2013. "Wrong Skewness and Finite Sample Correction in Parametric Stochastic Frontier Models," Center for Policy Research Working Papers 154, Center for Policy Research, Maxwell School, Syracuse University.
    3. Sungwon Lee & Young Lee, 2014. "Stochastic frontier models with threshold efficiency," Journal of Productivity Analysis, Springer, vol. 42(1), pages 45-54, August.
    4. 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.
    5. William C. Horrace & Christopher F. Parmeter, 2018. "A Laplace stochastic frontier model," Econometric Reviews, Taylor & Francis Journals, vol. 37(3), pages 260-280, March.
    6. Wikström, Daniel, 2012. "The fixed effects estimator of technical efficiency," Department of Economics publications 9101, Swedish University of Agricultural Sciences, Department of Economics.
    7. 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.
    8. Feng, Qu & Horrace, William C., 2012. "Estimating technical efficiency in micro panels," Economics Letters, Elsevier, vol. 117(3), pages 730-733.
    9. Zhang, Hongsong, 2013. "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.

    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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