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Alternative Technical Efficiency Measures: Skew, Bias, and Scale

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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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Bibliographic Info

Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 121.

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Length: 30 pages
Date of creation: Mar 2010
Date of revision:
Handle: RePEc:max:cprwps:121

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Keywords: stochastic frontier model; relative efficiency measure; two-sided measure; bias; bootstrap confidence intervals;

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  1. Myungsup Kim & Yangseon Kim & Peter Schmidt, 2006. "On the Accuracy of Bootstrap Confidence Intervals for Efficiency Levels in Stochastic Frontier Models with Panel Data," Working Papers 0704, University of Crete, Department of Economics.
  2. Hall, P. & Hardle, W. & Simar, L., 1991. "On teh inconsistency of bootstrap distribution estimators," CORE Discussion Papers 1991020, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. SIMAR, Léopold, . "Estimating efficiencies from frontier models with panel data: a comparison of parametric, non-parametric and semi-parametric metods with bootstrapping," CORE Discussion Papers RP -995, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. SIMAR, Léopold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. 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.
  6. 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.
  7. Koop, G. & Osiewalski, J. & Steel, M. F. J., . "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," CORE Discussion Papers RP -1245, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. 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.
  9. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
  10. 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.
  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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Cited by:
  1. 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.
  2. Young Hoon Lee & Sungwon Lee, 2011. "Stochastic Frontier Models with Threshold Efficiency," Working Papers 1205, Research Institute for Market Economy, Sogang University.
  3. Feng, Qu & Horrace, William C., 2012. "Estimating technical efficiency in micro panels," Economics Letters, Elsevier, vol. 117(3), pages 730-733.
  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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