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Ranking sectors using fuzzy output multipliers

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  • Malcolm Beynon
  • Max Munday
  • Annette Roberts

Abstract

Using input-output analysis to model the effects of changes in industry final demands is fraught with problems, many of which relate to the fundamental limitations of the concomitant linear framework. A further issue concerns the accuracy of the results, a consequence of the uncertainty surrounding the values of multipliers. Such uncertainty can create problems where the values of output multipliers are used to inform resource directions. This paper utilizes (and develops) a fuzzy input-output model and investigates the ranking of industries based on fuzzy output multipliers. The non-triviality of the fuzzy model is exposited in a general problem, where imprecision is defined by a proportional level of imprecision (fuzziness) in the technical coefficients. Through a nascent method for ranking fuzzy numbers, comparisons are made between the fuzzy and more traditional (non-fuzzy) analysis.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Economic Systems Research.

Volume (Year): 17 (2005)
Issue (Month): 3 ()
Pages: 237-253

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Handle: RePEc:taf:ecsysr:v:17:y:2005:i:3:p:237-253

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Related research

Keywords: Fuzzy set theory; fuzzy output multipliers; input-output; key sector analysis;

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Cited by:
  1. Beynon, Malcolm J. & Munday, Max, 2008. "Considering the effects of imprecision and uncertainty in ecological footprint estimation: An approach in a fuzzy environment," Ecological Economics, Elsevier, vol. 67(3), pages 373-383, October.
  2. Malcolm Beynon & Mark Clatworthy, 2013. "A fuzzy-based approach to residual income equity valuation," Review of Quantitative Finance and Accounting, Springer, vol. 40(4), pages 675-690, May.

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