Neural network forecasts of input-output technology

C.T. Papadas, George Hutchinson

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1607-1615
Number of pages9
JournalApplied Economics
Volume34
Issue number13
DOIs
Publication statusPublished - 10 Sep 2002

ASJC Scopus subject areas

  • Economics and Econometrics

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