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Neural network forecasts of input-output technology

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  • Christos Papadas
  • W. George Hutchinson

Abstract

A significant part of the literature on input-output (IO) analysis is dedicated to the development and application of methodologies forecasting and updating technology coefficients and multipliers. Prominent among such techniques is the RAS method, while more information demanding econometric methods, as well as other less promising ones, have been proposed. However, there has been little interest expressed in the use of more modern and often more innovative methods, such as neural networks in IO analysis in general. This study constructs, proposes and applies a Backpropagation Neural Network (BPN) with the purpose of forecasting IO technology coefficients and subsequently multipliers. The RAS method is also applied on the same set of UK IO tables, and the discussion of results of both methods is accompanied by a comparative analysis. The results show that the BPN offers a valid alternative way of IO technology forecasting and many forecasts were more accurate using this method. Overall, however, the RAS method outperformed the BPN but the difference is rather small to be systematic and there are further ways to improve the performance of the BPN.

Suggested Citation

  • Christos Papadas & W. George Hutchinson, 2002. "Neural network forecasts of input-output technology," Applied Economics, Taylor & Francis Journals, vol. 34(13), pages 1607-1615.
    • Handle: RePEc:taf:applec:v:34:y:2002:i:13:p:1607-1615
    DOI: 10.1080/00036840110118133
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    Cited by:

    1. Daniel Santin, 2008. "On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques," Applied Economics Letters, Taylor & Francis Journals, vol. 15(8), pages 597-600.
    2. Michael Dietrich, 2006. "Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72," Working Papers 2006007, The University of Sheffield, Department of Economics, revised May 2006.
    3. Michael Dietrich, 2005. "Using simple neural networks to analyse firm activity," Working Papers 2005014, The University of Sheffield, Department of Economics, revised Jul 2005.
    4. Bi-Huei Tsai, 2017. "Predicting the competitive relationships of industrial production between Taiwan and China using Lotka–Volterra model," Applied Economics, Taylor & Francis Journals, vol. 49(25), pages 2428-2442, May.

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