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GRAS versus minimizing absolute and squared differences: a comment


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  • Jan Oosterhaven


Junius and Oosterhaven (2003) developed the GRAS algorithm that minimizes the information gain when updating input-output tables with both positive and negative signs. Jackson and Murray (2004), however, claim that minimizing squared differences in coefficients produces a smaller information gain, which is theoretically impossible. In this comment, calculation errors are sorted out from differences in measures, and it is shown that the information gain needs to be taken in absolute terms when increasing and decreasing cell values occur together. The numerical results show that GRAS outperforms both sign-preserving alternatives in all but one comparison of lesser economic importance. Moreover, as opposed to the result of Jackson and Murray, they show that minimizing absolute differences consistently outperforms minimizing squared differences, which overweighs large errors in small coefficients.

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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: 327-331

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Handle: RePEc:taf:ecsysr:v:17:y:2005:i:3:p:327-331

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Keywords: RAS; biproportional updating; input-output; information gain;


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Cited by:
  1. Pieters, Janneke, 2010. "Growth and Inequality in India: Analysis of an Extended Social Accounting Matrix," World Development, Elsevier, vol. 38(3), pages 270-281, March.
  2. AlarcoĢn, Jorge & Ernst, Christoph & Khondker, Bazlul & Sharma, PD, 2011. "Dynamic social accounting matrix (DySAM) : concept, methodology and simulation outcomes: the case of Indonesia and Mozambique," ILO Working Papers 464252, International Labour Organization.


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