Volume 2, Issue 2, June 2009, Pages 132 - 139
Global Approximations to Cost and Production Functions using Artificial Neural Networks
- Efthymios G. Tsionas, Panayotis G. Michaelides, Angelos T. Vouldis
- Corresponding Author
- Efthymios G. Tsionas
Available Online 16 June 2009.
- https://doi.org/10.2991/ijcis.2009.2.2.4How to use a DOI?
- Neural networks, Econometrics, Production and Cost Functions, RTS, TFP.
- 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 modelâ€™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.
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 -