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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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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 27 (2012)
Issue (Month): 2 (03)
Pages: 253-268

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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. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
  3. 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).
  4. 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.
  5. HALL, Peter & HÄRDLE, Wolfgang & SIMAR, Léopold, . "On the inconsistency of bootstrap distribution estimators," CORE Discussion Papers RP -1062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. 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.
  7. 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.
  8. 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.
  9. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
  10. 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.
  11. 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.
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Cited by:
  1. Young Hoon Lee & Sungwon Lee, 2011. "Stochastic Frontier Models with Threshold Efficiency," Working Papers 1205, Research Institute for Market Economy, Sogang University.
  2. Feng, Qu & Horrace, William C., 2012. "Estimating technical efficiency in micro panels," Economics Letters, Elsevier, vol. 117(3), pages 730-733.
  3. 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.
  4. 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.
  5. 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.
  6. William Horrace & Seth Richards-Shubik, 2013. "Expected Efficiency Ranks From Parametric Stochastic Fronteir Models," Center for Policy Research Working Papers 153, Center for Policy Research, Maxwell School, Syracuse University.

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