Santín González, Daniel and Valiño Castro, Aurelia (2002) Comparing neural networks and efficiency techniques in nonlinear production functions. [ Documentos de Trabajo de la Facultad de Ciencias Económicas y Empresariales; nº 02, 2002, ISSN: 22555471 ]

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Abstract
Nonlinear production functions are common in economic theory and in real life, especially in cases with increasing and diminishing returns to scale but there are also contexts where an increase in one input implies a decrease in one output. The aim of this paper is to test how nonlinearity affect estimations of technical efficiency obtained by ordinary and corrected least squares (OLS, COLS), data envelopment analysis with constant and variables returns to scale (DEAcrs, DEAvrs), stochastic frontier analysis (SFA) and by multilayer perceptron neural networks with backpropagation (MLP). To do this we will construct a very simple nonlinear one inputone output production function and we will obtain different synthetic data with 50, 100, 200 and 300 decisionmaking units (DMUs). Afterwards we will add up different quantities of noise to the data and finally we will compare real efficiency with estimated values for all techniques named before among the different scenarios. Our results suggest that MLP is a flexible tool to fit production functions and a possible alternative to traditional techniques under nonlinear contexts.
Item Type:  Working Paper or Technical Report 

Uncontrolled Keywords:  Análisis funcional no lineal, Nonlinear production function, Technical efficiency, Artificial neural networks. 
Subjects:  Social sciences > Economics > Economics 
Series Name:  Documentos de Trabajo de la Facultad de Ciencias Económicas y Empresariales 
Volume:  2002 
Number:  02 
ID Code:  6763 
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Deposited On:  30 Nov 2007 
Last Modified:  02 Nov 2015 13:55 
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