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Explaining Long- and Short-Run Interactions in Time Series Data

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Author Info
Picci, Lucio

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Abstract

In this article, I extend the concept of separate cointegration to include the common-feature trend-cycle decomposition approach. This combined approach operates a reduction of the parameter space and permits the identification of the time series long- and short-run constituent factors. A careful assessment of their reciprocal relations, in turn, allows for the answering of potentially interesting economic questions. To show the usefulness of the proposed methodology, I apply it to the study of the relationships between the international business cycle and trade flows.

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Publisher Info
Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 19 (2001)
Issue (Month): 1 (January)
Pages: 85-94
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Handle: RePEc:bes:jnlbes:v:19:y:2001:i:1:p:85-94

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This page was last updated on 2009-11-22.


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