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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Paper provided by University of Exeter, School of Business and Economics in its series Discussion Papers with number
99/16.
Length: 11 pages Date of creation: 1999 Date of revision: Handle: RePEc:fth:exetec:99/16
Contact details of provider: Postal: School of Business and Economics University of Exeter Streatham Court Rennes Drive Exeter EX4 4PU Phone: (01392) 263218 Fax: (01392) 263242 Web page: http://www.exeter.ac.uk/sobe/ More information through EDIRC
Find related papers by JEL classification: C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods 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 D24 - Microeconomics - - Production and Organizations - - - Production; Capital and Total Factor Productivity; Capacity
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