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

Author

Listed:
  • Hailin Liao
  • Bin Wang
  • Tom Weyman-Jones

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 function. 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 ability of ANNs 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.

Suggested Citation

  • Hailin Liao & Bin Wang & Tom Weyman-Jones, 2007. "Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition," Global Economic Review, Taylor & Francis Journals, vol. 36(4), pages 361-384.
    • Handle: RePEc:taf:glecrv:v:36:y:2007:i:4:p:361-384
    DOI: 10.1080/12265080701694561
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    1. Mr. Michael Sarel, 1997. "Growth and Productivity in Asean Countries," IMF Working Papers 1997/097, International Monetary Fund.
    2. Hertel,Thomas W. (ed.), 1999. "Global Trade Analysis," Cambridge Books, Cambridge University Press, number 9780521643740, Enero-Abr.
    3. Michael Hobday, 1995. "Innovation In East Asia," Books, Edward Elgar Publishing, number 226.
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    Cited by:

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    2. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.

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    More about this item

    Keywords

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    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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