Backpropagation Neural Network versus Translog Model in Stochastic Frontiers: a Note Carlo Compatrison
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.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||1999|
|Contact details of provider:|| Postal: Streatham Court, Rennes Drive, Exeter EX4 4PU|
Phone: (01392) 263218
Fax: (01392) 263242
Web page: http://business-school.exeter.ac.uk/about/departments/economics/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:exe:wpaper:9916. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Carlos Cortinhas)
If references are entirely missing, you can add them using this form.