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Bootstrap-based testing for network DEA: Some Theory and Applications

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Abstract

Traditional data envelopment analysis (DEA) views a production technology process as a ‘black box’, while network DEA allows a researcher to look into the ‘black box’, to evaluate the overall performance and the performance of each sub-process of the system. The technical efficiency scores calculated from these approaches can be slightly, or sometimes vastly different. Our aim is to develop two bootstrap-based algorithms to test whether any observed difference between the results from the two approaches is statistically significant, or whether it is due to sampling and estimation noise. We focus on testing the equality of the first moment (i.e., the mean) and of the entire distribution of the technical efficiency scores. The bootstrap-based procedures can also be used for pairwise comparison between two network DEA models to perform sensitivity analysis of the resulting estimates across various network structures. In our empirical illustration of non-life insurance companies in Taiwan, both algorithms provide fairly robust results. We find statistical evidence suggesting that the first moment and the entire distribution of the overall technical efficiencies are significantly different between the DEA and network DEA models. However, the differences are not statistically significant for the two sub-processes across these models.

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  • Kelly D.T.Trinh & Valentin Zelenyuk, 2015. "Bootstrap-based testing for network DEA: Some Theory and Applications," CEPA Working Papers Series WP052015, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:103
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    File URL: https://economics.uq.edu.au/files/5106/WP052015.pdf
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    References listed on IDEAS

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    Cited by:

    1. Michali, Maria & Emrouznejad, Ali & Dehnokhalaji, Akram & Clegg, Ben, 2023. "Subsampling bootstrap in network DEA," European Journal of Operational Research, Elsevier, vol. 305(2), pages 766-780.
    2. Moriah B. Bostian & Cinzia Daraio & Rolf Fare & Shawna Grosskopf & Maria Grazia Izzo & Luca Leuzzi & Giancarlo Ruocco & William L. Weber, 2018. "Inference for Nonparametric Productivity Networks: A Pseudo-likelihood Approach," DIAG Technical Reports 2018-06, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

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    Keywords

    DEA; Network DEA; Subsampling Bootstrap;
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