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The measurement of technical efficiency: a neural network approach

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  • Daniel Santin
  • Francisco Delgado
  • Aurelia Valino

Abstract

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

Suggested Citation

  • Daniel Santin & Francisco Delgado & Aurelia Valino, 2004. "The measurement of technical efficiency: a neural network approach," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 627-635.
  • Handle: RePEc:taf:applec:v:36:y:2004:i:6:p:627-635
    DOI: 10.1080/0003684042000217661
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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. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    3. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April.
    4. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    5. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-364, Oct.-Dec..
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    Cited by:

    1. Daniel Santin, 2008. "On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques," Applied Economics Letters, Taylor & Francis Journals, vol. 15(8), pages 597-600.
    2. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
    4. Alves, André Bernardo & Wanke, Peter & Antunes, Jorge & Chen, Zhongfei, 2020. "Endogenous network efficiency, macroeconomy, and competition: Evidence from the Portuguese banking industry," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    5. Ti-Ching Peng, 2021. "The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(1), pages 1-32, February.
    6. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
    7. George Halkos & Mike G. Tsionas, 2019. "Accounting for Heterogeneity in Environmental Performance Using Data Envelopment Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1005-1025, October.
    8. Michael Dietrich, 2006. "Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72," Working Papers 2006007, The University of Sheffield, Department of Economics, revised May 2006.
    9. Vouldis, Angelos T. & Michaelides, Panayotis G. & Tsionas, Efthymios G., 2010. "Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML," Economic Modelling, Elsevier, vol. 27(3), pages 697-704, May.
    10. Francisco J. Delgado, 2005. "Measuring efficiency with neural networks. An application to the public sector," Economics Bulletin, AccessEcon, vol. 3(15), pages 1-10.
    11. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    12. Michael Dietrich, 2005. "Using simple neural networks to analyse firm activity," Working Papers 2005014, The University of Sheffield, Department of Economics, revised Jul 2005.
    13. Lee, Jooh & Kwon, He-Boong, 2017. "Progressive performance modeling for the strategic determinants of market value in the high-tech oriented SMEs," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 91-102.
    14. repec:ebl:ecbull:v:3:y:2005:i:15:p:1-10 is not listed on IDEAS
    15. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2010. "Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks," European Journal of Operational Research, Elsevier, vol. 206(2), pages 479-487, October.
    16. Azadeh, A. & Ghaderi, S.F. & Anvari, M. & Saberi, M., 2007. "Performance assessment of electric power generations using an adaptive neural network algorithm," Energy Policy, Elsevier, vol. 35(6), pages 3155-3166, June.
    17. Shivi Agarwal, 2016. "DEA-neural networks approach to assess the performance of public transport sector of India," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 248-258, June.

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