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

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Author Info

  • 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.

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Bibliographic Info

Paper provided by Exeter University, Department of Economics in its series Discussion Papers with number 9916.

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Length: 11 pages
Date of creation: 1999
Date of revision:
Handle: RePEc:exe:wpaper:9916

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Web page: http://business-school.exeter.ac.uk/about/departments/economics/
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Related research

Keywords: ECONOMETRICS ; STATISTICAL ANALYSIS ; NETWORK ANALYSIS;

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Cited by:
  1. 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. 0, pages 337-356, November.

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