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

Hailin Liao (), Bin Wang and Tom Weyman-Jones

Global Economic Review, 2007, vol. 36, issue 4, 361-384

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.

Keywords: Total factor productivity; neural networks; stochastic frontier analysis; DEA; East Asian economies (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/12265080701694561 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:glecrv:v:36:y:2007:i:4:p:361-384

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RGER20

DOI: 10.1080/12265080701694561

Access Statistics for this article

Global Economic Review is currently edited by Kap-Young Jeong and Taeyoon Sung

More articles in Global Economic Review from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().