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Measuring Technical Efficiency with Neural Networks: a Review

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  • Santín, Daniel
  • Delgado, Francisco
  • Valiño, Aurelia

Abstract

The main purpose of this paper is to provide an introduction to artificial neural networks (ANNs) and to review their applications on efficiency analysis. Finally, a comparison of efficiency techniques in a non-linear production function is carried out. Our results suggest that ANNs are a promising alternative to traditional approaches, econometric models and non-parametric methods such as data envelopment analysis (DEA), to fit production functions and measure efficiency under non-linear contexts.

Suggested Citation

  • Santín, Daniel & Delgado, Francisco & Valiño, Aurelia, 2001. "Measuring Technical Efficiency with Neural Networks: a Review," Efficiency Series Papers 2001/09, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
  • Handle: RePEc:oeg:wpaper:2001/09
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    File URL: https://www.unioviedo.es/oeg/ESP/esp_2001_09.pdf
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    References listed on IDEAS

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    1. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    2. Baker, Bruce D., 2001. "Can flexible non-linear modeling tell us anything new about educational productivity?," Economics of Education Review, Elsevier, vol. 20(1), pages 81-92, February.
    3. 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.
    4. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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