Explaining long- and short-run interactions in time series data

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 hows.