Explaining Long- and Short-Run Interactions in Time Series Data
扩展了协整概念，结合共同特征趋势-周期分解方法，识别时间序列的长期和短期构成因素，并应用于研究国际商业周期与贸易流动的关系。
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.