Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition

Hailin Liao (), Bin Wang () and Tom Weyman-Jones ()
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Bin Wang: Dept of Economics, Loughborough University
Tom Weyman-Jones: Dept of Economics, Loughborough University

Discussion Paper Series from Department of Economics, Loughborough University

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.

Keywords: total factor productivity; neural networks; stochastic frontier analysis; DEA; East Asian economies (search for similar items in EconPapers)
JEL-codes: C45 D24 L60 O47 O53 (search for similar items in EconPapers)
Date: 2007-11, Revised 2007-11
New Economics Papers: this item is included in nep-cmp, nep-eff and nep-sea
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