Neural network based models for efficiency frontier analysis: An application to East Asian economies' growth decomposition

Hailin Liao, Bin Wang, Tom Weyman-Jones

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)361-384
Number of pages24
JournalGlobal Economic Review
Volume36
Issue number4
DOIs
Publication statusPublished - 9 Nov 2007
Externally publishedYes

Keywords

  • Dea
  • East asian economies
  • Neural networks
  • Stochastic frontier analysis
  • Total factor productivity

ASJC Scopus subject areas

  • Business and International Management
  • Political Science and International Relations
  • Economics, Econometrics and Finance(all)

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