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


  • Picci, Lucio


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.

Suggested Citation

  • Picci, Lucio, 2001. "Explaining Long- and Short-Run Interactions in Time Series Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 85-94, January.
  • Handle: RePEc:bes:jnlbes:v:19:y:2001:i:1:p:85-94

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    Cited by:

    1. Kurita, Takamitsu, 2019. "Separate cointegration in a VAR system subject to structural breaks," Economics Letters, Elsevier, vol. 179(C), pages 19-23.

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