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Double Bootstrap Confidence Intervals in the Two-Stage DEA Approach

Author

Listed:
  • Neil Kellard
  • Denise Osborn
  • Jerry Coakley
  • Dimitris K. Chronopoulos
  • Claudia Girardone
  • John C. Nankervis

Abstract

type="main" xml:id="jtsa12122-abs-0001"> Contextual factors usually assume an important role in determining firms' productive efficiencies. Nevertheless, identifying them in a regression framework might be complicated. The problem arises from the efficiencies being correlated with each other when estimated by Data Envelopment Analysis, rendering standard inference methods invalid. Simar and Wilson (2007) suggest the use of bootstrap algorithms that allow for valid statistical inference in this context. This article extends their work by proposing a double bootstrap algorithm for obtaining confidence intervals with improved coverage probabilities. Moreover, acknowledging the computational burden associated with iterated bootstrap procedures, we provide an algorithm based on deterministic stopping rules, which is less computationally demanding. Monte Carlo evidence shows considerable improvement in the coverage probabilities after iterating the bootstrap procedure. The results also suggest that percentile confidence intervals perform better than their basic counterpart.

Suggested Citation

  • Neil Kellard & Denise Osborn & Jerry Coakley & Dimitris K. Chronopoulos & Claudia Girardone & John C. Nankervis, 2015. "Double Bootstrap Confidence Intervals in the Two-Stage DEA Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 653-662, September.
    • Handle: RePEc:bla:jtsera:v:36:y:2015:i:5:p:653-662
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    File URL: http://hdl.handle.net/10.1111/jtsa.12122
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    References listed on IDEAS

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    1. Mei Xue & Patrick T. Harker, 1999. "Overcoming the Inherent Dependency of DEA Efficiency Scores: A Bootstrap Approach," Center for Financial Institutions Working Papers 99-17, Wharton School Center for Financial Institutions, University of Pennsylvania.
    2. Berger, Allen N. & Humphrey, David B., 1997. "Efficiency of financial institutions: International survey and directions for future research," European Journal of Operational Research, Elsevier, vol. 98(2), pages 175-212, April.
    3. Kneip, Alois & Park, Byeong U. & Simar, Léopold, 1998. "A Note On The Convergence Of Nonparametric Dea Estimators For Production Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 14(6), pages 783-793, December.
    4. Staub, Roberta B. & da Silva e Souza, Geraldo & Tabak, Benjamin M., 2010. "Evolution of bank efficiency in Brazil: A DEA approach," European Journal of Operational Research, Elsevier, vol. 202(1), pages 204-213, April.
    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. Wang, Mei-Hui & Huang, Tai-Hsin, 2007. "A study on the persistence of Farrell's efficiency measure under a dynamic framework," European Journal of Operational Research, Elsevier, vol. 180(3), pages 1302-1316, August.
    7. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    8. Nankervis, John C., 2005. "Computational algorithms for double bootstrap confidence intervals," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 461-475, April.
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    Cited by:

    1. Song, Yao-yao & Li, Jing-jing & Wang, Jin-li & Yang, Guo-liang & Chen, Zhenling, 2022. "Eco-efficiency of Chinese transportation industry: A DEA approach with non-discretionary input," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    2. Chen, Zhongfei & Matousek, Roman & Wanke, Peter, 2018. "Chinese bank efficiency during the global financial crisis: A combined approach using satisficing DEA and Support Vector Machines☆," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 71-86.
    3. Fukuyama, Hirofumi & Matousek, Roman, 2017. "Modelling bank performance: A network DEA approach," European Journal of Operational Research, Elsevier, vol. 259(2), pages 721-732.
    4. Abdul Latif Alhassan & Michael Lawer Tetteh, 2017. "Non-Interest Income and Bank Efficiency in Ghana: A Two-Stage DEA Bootstrapping Approach," Journal of African Business, Taylor & Francis Journals, vol. 18(1), pages 124-142, January.
    5. Yang, Guo-liang & Fukuyama, Hirofumi & Song, Yao-yao, 2019. "Estimating capacity utilization of Chinese manufacturing industries," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 94-110.

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