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Neural network forecasts of input-output technology

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dc.contributor.author Papadas, CT en
dc.contributor.author Hutchinson, WG en
dc.date.accessioned 2014-06-06T06:45:06Z
dc.date.available 2014-06-06T06:45:06Z
dc.date.issued 2002 en
dc.identifier.issn 00036846 en
dc.identifier.uri http://dx.doi.org/10.1080/00036840110118133 en
dc.identifier.uri http://62.217.125.90/xmlui/handle/123456789/2251
dc.subject.other input-output analysis en
dc.title Neural network forecasts of input-output technology en
heal.type journalArticle en
heal.identifier.primary 10.1080/00036840110118133 en
heal.publicationDate 2002 en
heal.abstract A significant part of the literature on input-output (IO) analysis is dedicated to the development and application of methodologiest 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. en
heal.journalName Applied Economics en
dc.identifier.issue 13 en
dc.identifier.volume 34 en
dc.identifier.doi 10.1080/00036840110118133 en
dc.identifier.spage 1607 en
dc.identifier.epage 1615 en


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