Global Approximations to Cost and Production Functions using Artificial Neural Networks (original) (raw)

2009, International Journal of Computational Intelligence Systems

Most business decisions depend on accurate approximations to the cost and production functions. Traditionally, 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 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 scale economies and total factor productivity may be computed routinely. . Dr. Michaelides was recipient of three scholarships for academic excellence and has received the NTUA award for the promotion of Sciences. Also, he has supervised numerous Diploma Theses and has participated in numerous research projects with an active role. His research relates to many fields of economics, econometrics and applied mathematics. He is member of a variety of scientific organizations.