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A convenient policy control through the Macro Multiplier Approach

Listed author(s):
  • Maurizio Ciaschini

    (not available)

  • Rosita Pretaroli

    (not available)

  • Claudio Socci

    (Unversità di Macerata)

In this paper an attempt is made to identify a ”convenient” structure of a policy variable, final demand control, through the use of a multi-sectoral model. The method used relies on a specific spectral ecomposition which allows for the quantification of the scale-effect of each structure that the policy variable can assume on the structures of the objective ariable. This quantification is of aggregated type since the scalars obtained are valid for all sectoral components of both the policy variable and the objective variable. What is more relevant they are consistent with the multi-sectoral feature of the model, overcoming the objections put forward by the theory of aggregation. In fact the aggregation theory states that if we aggregate sectors we obtain a new model with different structural properties, while, in our case, the aggregated scalar that we obtain for each structure is perfectly consistent with the original model. We call these scalars Macroeconomic Multipliers since they say how many time the modulus of the multi-sectoral policy variable is multiplied when we compare it with the modulus of the effects observed on the multi-sectoral objective variable. Once identified the structures and the associated Macro Multipliers, the policy maker can have a complete picture of the economic structure of the objective variables that can be attained and determine a ”convenient” structure of the policy variable choosing either one structure or a combination of the structures identified.

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Paper provided by Macerata University, Department of Studies on Economic Development (DiSSE) in its series Working Papers with number 02-2006.

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Date of creation: Jul 2006
Date of revision: Nov 2008
Handle: RePEc:mcr:wpaper:wpaper00002
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  1. Ciaschini, Maurizio, 1989. "Scale and structure in economic modelling," Economic Modelling, Elsevier, vol. 6(4), pages 355-373, October.
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