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The measurement of technical efficiency: a neural network approach

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Author Info
Daniel Santín
Francisco J. Delgado
Aurelia Valiño

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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.

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Publisher Info
Article provided by Taylor and Francis Journals in its journal Applied Economics.

Volume (Year): 36 (2004)
Issue (Month): 6 (April)
Pages: 627-635
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Handle: RePEc:taf:applec:v:36:y:2004:i:6:p:627-635

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  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. [Downloadable!] (restricted)
  2. 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. [Downloadable!] (restricted)
  3. 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 347-64, Oct.-Dec.. [Downloadable!] (restricted)
  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. [Downloadable!] (restricted)
  5. Chung-Ming Kuan & Halbert White, 1992. "Artificial Neural Networks: An Econometric Perspective," University of California at San Diego, Economics Working Paper Series 92-11, Department of Economics, UC San Diego.
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Francisco J. Delgado, 2005. "Measuring efficiency with neural networks. An application to the public sector," Economics Bulletin, Economics Bulletin, vol. 3(15), pages 1-10. [Downloadable!]
  2. Michael Dietrich, 2005. "Using simple neural networks to analyse firm activity," Working Papers 2005014, The University of Sheffield, Department of Economics, revised Jul 2005. [Downloadable!]
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