Comparing neural networks and efficiency techniques in non-linear production functions
Non-linear 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 non-linearity 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 non-linear one input-one output production function and we will obtain different synthetic data with 50, 100, 200 and 300 decision-making 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 non-linear contexts
|Date of creation:||2002|
|Date of revision:|
|Contact details of provider:|| Phone: 913942604|
Web page: http://economicasyempresariales.ucm.es/Email:
More information through EDIRC
|Order Information:|| Postal: Facultad de Ciencias Económicas y Empresariales. Campus de Somosaguas, 28223 - POZUELO DE ALARCÓN (MADRID)|
Web: https://economicasyempresariales.ucm.es/working-papers-ccee Email:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
When requesting a correction, please mention this item's handle: RePEc:ucm:doctra:02-02. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Águeda González Abad)
If references are entirely missing, you can add them using this form.