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Backpropagation Neural Network versus Translog Model in Stochastic Frontiers: a Note Carlo Compatrison

Author

Listed:
  • Guermat, C.
  • Hadri, K.

Abstract

Little attention has been given to the effects of functional form mis-specification on the estimation of stochastic frontier models and to the possibility of using backpropagation neural netwok as a flexible functional form to approximate the production or cost functions. This paper has two main aims. First, it uses Monte Carlo experimentation to investigate the effects of functional form mis-specification on the finite sample properties of the maximum likelihod (ML) estimators of the half-normal stochastic frontier production functions; second it compared the performance of backpropagation neural network with that of translog.

Suggested Citation

  • Guermat, C. & Hadri, K., 1999. "Backpropagation Neural Network versus Translog Model in Stochastic Frontiers: a Note Carlo Compatrison," Discussion Papers 9916, Exeter University, Department of Economics.
  • Handle: RePEc:exe:wpaper:9916
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    References listed on IDEAS

    as
    1. Sawa, Takamitsu, 1972. "Finite-Sample Properties of the k-Class Estimators," Econometrica, Econometric Society, vol. 40(4), pages 653-680, July.
    2. Phillips, G. D. A. & Harvey, A. C., 1984. "A note on estimating and testing exogenous variable coefficient estimators in simultaneous equation models," Economics Letters, Elsevier, vol. 15(3-4), pages 301-307.
    3. Kinal, Terrence W, 1980. "The Existence of Moments of k-Class Estimators," Econometrica, Econometric Society, vol. 48(1), pages 241-249, January.
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    Citations

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    Cited by:

    1. Santin, Daniel, 2004. "On the Approximation of Production Functions: A Comparison of Artificial Neural Networks Frontiers and Efficiency Techniques," Efficiency Series Papers 2004/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    2. Kaddour Hadri & Julie Whittaker, 1999. "Efficiency, Environmental Contaminants and Farm Size: Testing for Links Using Stochastic Production Frontiers," Journal of Applied Economics, Universidad del CEMA, vol. 2, pages 337-356, November.
    3. 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.

    More about this item

    Keywords

    ECONOMETRICS ; STATISTICAL ANALYSIS ; NETWORK ANALYSIS;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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