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Neural network based models for efficiency frontier analysis: an application to East Asian economies' growth decomposition
preprint
posted on 2009-02-06, 13:27 authored by Hailin Liao, Bin Wang, Tom Weyman-JonesThere has been a long tradition in business and economics to use frontier analysis to assess a production unit’s performance. The first attempt utilized the data envelopment analysis (DEA) which is based on a piecewise linear and mathematical programming approach, whilst the other employed the parametric approach to estimate the stochastic frontier functions. Both approaches have their advantages as well as limitations. This paper sets out to use an alternative approach, i.e. artificial neural networks (ANNs) for measuring efficiency and productivity growth for seven East Asian economies at manufacturing level, for the period 1963 to 1998, and the relevant comparisons are carried out between DEA and ANN, and stochastic frontier analysis (SFA) and ANN in order to test the ANNs’ ability to assess the performance of production units. The results suggest that ANNs are a promising alternative to traditional approaches, to approximate production functions more accurately and measure efficiency and productivity under non-linear contexts, with minimum assumptions.
History
School
- Business and Economics
Department
- Economics
Publisher
© Loughborough UniversityVersion
- VoR (Version of Record)
Publication date
2007Notes
This is a working paper. It is also available at: http://ideas.repec.org/p/lbo/lbowps/2007_24.htmlISSN
1750-4171Book series
Loughborough University. Department of Economics. Discussion Paper Series;WP 2007 - 24Language
- en