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Profit Efficiency, DEA, FDH and Big Data

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Abstract

The goal of this article is to outline a very simple way of estimating profit efficiency in the DEA and FDH frameworks, but avoiding the computational burden of linear programming. With this result it is possible to compute profit efficiency even when dimension of inputs and outputs are larger than the dimension of number of decision making units (firms, individuals, etc.), as is often the case in the `big data'.

Suggested Citation

  • Valentin Zelenyuk, 2018. "Profit Efficiency, DEA, FDH and Big Data," CEPA Working Papers Series WP042018, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:125
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    File URL: https://economics.uq.edu.au/files/6190/WP042018.pdf
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    1. Rolf Färe & Xinju He & Sungko Li & Valentin Zelenyuk, 2019. "A Unifying Framework for Farrell Profit Efficiency Measurement," Operations Research, INFORMS, vol. 67(1), pages 183-197, January.
    2. Cooper, W.W. & Pastor, Jesus T. & Aparicio, Juan & Borras, Fernando, 2011. "Decomposing profit inefficiency in DEA through the weighted additive model," European Journal of Operational Research, Elsevier, vol. 212(2), pages 411-416, July.
    3. Aparicio, Juan & Ortiz, Lidia & Pastor, Jesus T., 2017. "Measuring and decomposing profit inefficiency through the Slacks-Based Measure," European Journal of Operational Research, Elsevier, vol. 260(2), pages 650-654.
    4. Aparicio, Juan & Pastor, Jesus T. & Ray, Subhash C., 2013. "An overall measure of technical inefficiency at the firm and at the industry level: The ‘lost profit on outlay’," European Journal of Operational Research, Elsevier, vol. 226(1), pages 154-162.
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