International Journal of Computational Intelligence Systems

Volume 2, Issue 2, June 2009, Pages 132 - 139

Global Approximations to Cost and Production Functions using Artificial Neural Networks

Authors
Efthymios G. Tsionas, Panayotis G. Michaelides, Angelos T. Vouldis
Corresponding Author
Efthymios G. Tsionas
Available Online 16 June 2009.
DOI
https://doi.org/10.2991/ijcis.2009.2.2.4How to use a DOI?
Keywords
Neural networks, Econometrics, Production and Cost Functions, RTS, TFP.
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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
2 - 2
Pages
132 - 139
Publication Date
2009/06
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.2009.2.2.4How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Efthymios G. Tsionas
AU  - Panayotis G. Michaelides
AU  - Angelos T. Vouldis
PY  - 2009
DA  - 2009/06
TI  - Global Approximations to Cost and Production Functions using Artificial Neural Networks
JO  - International Journal of Computational Intelligence Systems
SP  - 132
EP  - 139
VL  - 2
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2009.2.2.4
DO  - https://doi.org/10.2991/ijcis.2009.2.2.4
ID  - Tsionas2009
ER  -