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Title: Neural network based models for efficiency frontier analysis: an application to East Asian economies' growth decomposition
Authors: Liao, Hailin
Wang, Bin
Weyman-Jones, Thomas G.
Keywords: Total factor productivity
Neural networks
Stochastic frontier analysis
DEA
East Asian economies
Issue Date: 2007
Publisher: © Loughborough University
Series/Report no.: Loughborough University. Department of Economics. Discussion Paper Series;WP 2007 - 24
Abstract: There 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.
Description: This is a working paper. It is also available at: http://ideas.repec.org/p/lbo/lbowps/2007_24.html
Version: Published
URI: https://dspace.lboro.ac.uk/2134/4175
ISSN: 1750-4171
Appears in Collections:Working Papers (Economics)

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