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

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  • Hailin Liao

    ()
    (Dept of Economics, Loughborough University)

  • Bin Wang

    ()
    (Dept of Economics, Loughborough University)

  • Tom Weyman-Jones

    ()
    (Dept 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.

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Bibliographic Info

Paper provided by Department of Economics, Loughborough University in its series Discussion Paper Series with number 2007_24.

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Date of creation: Nov 2007
Date of revision: Nov 2007
Handle: RePEc:lbo:lbowps:2007_24

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Keywords: total factor productivity; neural networks; stochastic frontier analysis; DEA; East Asian economies;

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