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Resampling in DEA

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  • Kaoru Tone

    (National Graduate Institute for Policy Studies)

Abstract

In this paper, we propose new resampling models in data envelopment analysis (DEA). Input/output values are subject to change for several reasons, e.g., measurement errors, hysteretic factors, arbitrariness and so on. Furthermore, these variations differ in their input/output items and their decision-making units (DMU). Hence, DEA efficiency scores need to be examined by considering these factors. Resampling based on these variations is necessary for gauging the confidence interval of DEA scores. We propose three resampling models. The first one assumes downside and upside measurement error rates for each input/output, which are common to all DMUs. We resample data following the triangular distribution that the downside and upside errors indicate around the observed data. The second model utilizes historical data, e.g., past-present, for estimating data variations, imposing chronological order weights which are supplied by Lucas series (a variant of Fibonacci series). The last one deals with future prospects. This model aims at forecasting the future efficiency score and its confidence interval for each DMU.

Suggested Citation

  • Kaoru Tone, 2013. "Resampling in DEA," GRIPS Discussion Papers 13-23, National Graduate Institute for Policy Studies.
    • Handle: RePEc:ngi:dpaper:13-23
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    References listed on IDEAS

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    1. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    2. 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.
    3. Tziogkidis, Panagiotis, 2012. "Bootstrap DEA and Hypothesis Testing," Cardiff Economics Working Papers E2012/18, Cardiff University, Cardiff Business School, Economics Section.
    4. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. Xu, Bing & Ouenniche, Jamal, 2012. "A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices' volatility forecasting models," Energy Economics, Elsevier, vol. 34(2), pages 576-583.
    7. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, November.
    8. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
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    Cited by:

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    3. Ying Li & Yung-Ho Chiu & Tai-Yu Lin & Tzu-Han Chang, 2020. "Pre-Evaluating the Technical Efficiency Gains from Potential Mergers and Acquisitions in the IC Design Industry," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 525-559, April.
    4. Yung‐ho Chiu & Tai‐Yu Lin & Tzu‐Han Chang & Yi‐Nuo Lin & Shih‐Yung Chiu, 2021. "Prevaluating efficiency gains from potential mergers and acquisitions in the financial industry with the Resample Past–Present–Future data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(2), pages 369-384, March.
    5. Chang, Tsung-Sheng & Lin, Ji-Gang & Ouenniche, Jamal, 2023. "DEA-based Nash bargaining approach to merger target selection," European Journal of Operational Research, Elsevier, vol. 305(2), pages 930-945.
    6. Ying Li & Yung‐ho Chiu & Ying Yu Chen & Lihua Wang & Yi‐Nuo Lin & Su‐Wan Wang, 2022. "The impact of market share on efficiency of commercial banks: Resampling slacks‐based measure data envelopment analyses model," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(5), pages 1621-1634, July.
    7. Sommersguter-Reichmann, Margit & Stepan, Adolf, 2015. "The interplay between regulation and efficiency: Evidence from the Austrian hospital inpatient sector," Socio-Economic Planning Sciences, Elsevier, vol. 52(C), pages 10-21.
    8. Yan He & Yung-ho Chiu & Bin Zhang, 2020. "Prevaluating Technical Efficiency Gains From Potential Mergers and Acquisitions in China’s Coal Industry," SAGE Open, , vol. 10(3), pages 21582440209, July.
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