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A Note On Added Information In The Ras Procedure: Reexamination Of Some Evidence

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  • Louis De Mesnard
  • Ronald E. Miller
Abstract
ABSTRACT An example in Miernyk (1977) presented a rather counterintuitive result, namely, that introducing accurate exogenous information into an RAS matrix estimating procedure could lead to an estimate that was worse than one generated by RAS using no exogenous information at all. This became an oft‐cited black mark against RAS. Miller and Blair (1985) included a different (and small) illustration of the same possibility. It was recently pointed out by one of us that the Miller–Blair numerical results are wrong. For that reason, we decided to reexamine all the empirical evidence we could find on the subject. While figures in both Miernyk and Miller–Blair appear to be wrong, more recent published examples seem to have it right. In short, it is possible to identify examples in which additional (correct) information leads to poorer RAS estimates, at least under several fairly common metrics for comparing “closeness” of matrices. However, the overwhelming majority of the evidence is to the contrary. As a general rule, introduction of accurate exogenous information into RAS improves the resulting estimates, and counterexamples should probably not be taken too seriously.

Suggested Citation

  • Louis De Mesnard & Ronald E. Miller, 2006. "A Note On Added Information In The Ras Procedure: Reexamination Of Some Evidence," Journal of Regional Science, Wiley Blackwell, vol. 46(3), pages 517-528, August.
  • Handle: RePEc:bla:jregsc:v:46:y:2006:i:3:p:517-528
    DOI: 10.1111/j.1467-9787.2006.00450.x
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    Cited by:

    1. Umed Temurshoev & Marcel P. Timmer, 2011. "Joint estimation of supply and use tables," Papers in Regional Science, Wiley Blackwell, vol. 90(4), pages 863-882, November.
    2. Uwe Blien & Friedrich Graef, 2013. "The ADETON method," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 33(2), pages 135-150, October.
    3. He, Peijun & Ng, Tsan Sheng & Su, Bin, 2015. "Energy import resilience with input–output linear programming models," Energy Economics, Elsevier, vol. 50(C), pages 215-226.
    4. Gordon, Peter & Park, JiYoung & Richardson, Harry W., 2009. "Modeling input-output impacts with substitutions in the household sector: A numerical example," Economic Modelling, Elsevier, vol. 26(3), pages 696-701, May.
    5. repec:dgr:rugggd:gd-116 is not listed on IDEAS

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