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On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques

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  • Daniel Santin

Abstract

The aim of this article is to show how Artificial Neural Networks (ANN) is a valid semi-parametric alternative for fitting empirical production functions and measuring technical efficiency. To do this a Monte-Carlo experiment is carried out on a simulated smooth production technology for assessing efficiency results of ANN compared with Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). As ANNs provides average production function estimations this article proposes a so-called thick frontier strategy for transform average estimations into a productive frontier. Main advantages of ANN are in contexts where the production function is smooth, completely unknown, contains nonlinear relationships among variables and the quantity of noise and efficiency in data is moderate. Under this scenario, the results display that an ANNs algorithm can detect, better than traditional tools, the underlying shape of the production function from observed data.

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  • 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.
    • Handle: RePEc:taf:apeclt:v:15:y:2008:i:8:p:597-600
    DOI: 10.1080/13504850600721973
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    1. 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.
    2. 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.
    3. 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.

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