title: |
Global Approximations to Cost and Production Functions using Artificial Neural Networks |
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publication: |
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| volume-issue: | 2 - 2 | |
| pages: | 132 - 139 | |
ISSN: |
1875-6883 | |
DOI: |
doi:10.2991/ijcis.2009.2.2.4 (how to use a DOI) | |
author(s): |
Efthymios G. Tsionas, Panayotis G. Michaelides, Angelos T. Vouldis |
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publication date: |
June 2009 |
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keywords: |
Neural networks, Econometrics, Production and Cost Functions, RTS, TFP.
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abstract: |
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 (ANNs) 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 Returns to Scale (RTS) and Total Factor Productivity (TFP)
may be computed routinely.
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |