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Neural Networks for Approximating the Cost and Production Functions

Author

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  • Tsionas, Efthymios G.
  • Michaelides, Panayotis G.
  • Vouldis, Angelos

Abstract

Most business decisions depend on accurate approximations to the cost and production functions. Traditionally, the estimation of cost and production functions in economics relies on standard specifications which are less than satisfactory in numerous situations. However, instead of fitting the data with a pre-specified model, Artificial Neural Networks let the data itself serve as evidence to support the model’s estimation of the underlying process. In this context, the proposed approach combines the strengths of economics, statistics and machine learning research and the paper proposes a global approximation to arbitrary cost and production functions, respectively, given by ANNs. Suggestions on implementation are proposed and empirical application relies on standard techniques. All relevant measures such as scale economies and total factor productivity may be computed routinely.

Suggested Citation

  • Tsionas, Efthymios G. & Michaelides, Panayotis G. & Vouldis, Angelos, 2008. "Neural Networks for Approximating the Cost and Production Functions," MPRA Paper 74448, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:74448
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    References listed on IDEAS

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    1. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
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    4. Andreas Hornstein & Per Krusell, 1996. "Can Technology Improvements Cause Productivity Slowdowns?," NBER Chapters, in: NBER Macroeconomics Annual 1996, Volume 11, pages 209-276, National Bureau of Economic Research, Inc.
    5. Gallant, A. Ronald, 1982. "Unbiased determination of production technologies," Journal of Econometrics, Elsevier, vol. 20(2), pages 285-323, November.
    6. Diewert, W E, 1971. "An Application of the Shephard Duality Theorem: A Generalized Leontief Production Function," Journal of Political Economy, University of Chicago Press, vol. 79(3), pages 481-507, May-June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Neural networks; Econometrics; Production and Cost Functions; RTS; TFP.;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • D2 - Microeconomics - - Production and Organizations
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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