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Biproportional methods of structural change analysis: A typological survey

Author

Listed:
  • Louis de Mesnard

    (LEG - Laboratoire d'Economie et de Gestion - UB - Université de Bourgogne - CNRS - Centre National de la Recherche Scientifique)

Abstract

Analysts often are interested in learning how much an exchange system has changed over time or how two different exchange systems differ. Identifying structural difference in exchange matrices can be performed using either 'directed' or 'undirected' methods. Directed methods are based on the computation and comparison of column- or row-normalizations of the matrices. The choice of row or column for the normalization implies a specific direction of the exchanges, so that the column-wise normalized results should not be compared to the row-wise normalized results. In this category fall the simple comparison of coefficient matrices and the causative method. Undirected methods do not impose such underlying constraints on exchanges. Hence, I present a set of undirected methods that can be used to compare structural matrices: the biproportional ordinary filter, the biproportional mean filter and the bi-Markovian filter. While doing so, I recall why the bicausative method must be dismissed. I then classify the methods according to their orientation and data needs, and illustrate how the results can differ from one method to the next using French tables for 1980 and 1997.

Suggested Citation

  • Louis de Mesnard, 2004. "Biproportional methods of structural change analysis: A typological survey," Post-Print halshs-00068409, HAL.
    • Handle: RePEc:hal:journl:halshs-00068409
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    Cited by:

    1. Richard Wood, 2011. "Construction, Stability And Predictability Of An Input-Output Time-Series For Australia," Economic Systems Research, Taylor & Francis Journals, vol. 23(2), pages 175-211.
    2. Maite Blázquez & Carlos Llano & Julian Moral, 2010. "Commuting Times: Is There Any Penalty for Immigrants?," Urban Studies, Urban Studies Journal Limited, vol. 47(8), pages 1663-1686, July.
    3. Ryoko Morioka & Keisuke Nansai & Koji Tsuda, 2018. "Role of linkage structures in supply chain for managing greenhouse gas emissions," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 7(1), pages 1-21, December.
    4. Lourens Broersma & Ton Van Moergastel, 2007. "A Shortcut Method for Generating Time Series of Input Data for Productivity Analysis," Economic Systems Research, Taylor & Francis Journals, vol. 19(3), pages 277-293.

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