Neural Networks for Approximating the Cost and Production Functions

Mike Tsionas, Panayotis Michaelides and Angelos Vouldis

MPRA Paper from University Library of Munich, Germany

Abstract: 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.

Keywords: Neural networks; Econometrics; Production and Cost Functions; RTS; TFP. (search for similar items in EconPapers)
JEL-codes: C3 C45 C5 D2 D24 (search for similar items in EconPapers)
Date: 2008
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