Tsionas, Efthymios G. and Michaelides, Panayotis G. and Vouldis, Angelos (2008): Neural Networks for Approximating the Cost and Production Functions.

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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 prespecified 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.
Item Type:  MPRA Paper 

Original Title:  Neural Networks for Approximating the Cost and Production Functions 
Language:  English 
Keywords:  Neural networks, Econometrics, Production and Cost Functions, RTS, TFP. 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics C  Mathematical and Quantitative Methods > C5  Econometric Modeling D  Microeconomics > D2  Production and Organizations D  Microeconomics > D2  Production and Organizations > D24  Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity 
Item ID:  74448 
Depositing User:  Prof. Dr. Panayotis G. Michaelides 
Date Deposited:  12 Oct 2016 07:30 
Last Modified:  02 Oct 2019 04:47 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/74448 