This paper elaborates on the sectoral-regional view of the business cycle synchronization in the EU -- a necessary condition for the optimal currency area. We argue that complete and tidy clustering of the data improves the decision maker's understanding of the business cycle and, by extension, the quality of economic decisions. We define the business cycles by applying a wavelet approach to drift-adjusted gross value added data spanning over 2000Q1 to 2021Q2. For the application of the synchronization analysis, we propose the novel soft-clustering approach, which adjusts hierarchical clustering in several aspects. First, the method relies on synchronicity dissimilarity measures, noting that, for time series data, the feature space is the set of all points in time. Then, the ``soft'' part of the approach strengthens the synchronization signal by using silhouette measures. Finally, we add a probabilistic sparsity algorithm to drop out the most asynchronous ``noisy'' data improving the silhouette scores of the most and less synchronous groups. The method, hence, splits the sectoral-regional data into three groups: the synchronous group that shapes the EU business cycle; the less synchronous group that may hint at cycle forecasting relevant information; the asynchronous group that may help investors to diversify through-the-cycle risks of the investment portfolios. The results support the core-periphery hypothesis.
翻译:本文详细阐述了欧盟商业周期同步化的部门-区域观点,这是最佳货币区的一个必要条件。我们认为,数据完整和整洁的组合组合可以提高决策者对商业周期的理解,进而提高经济决策的质量。我们通过对2000Q1至2021Q2期间的漂移调整总增值数据采用波浪法来定义商业周期。关于同步分析的应用,我们建议采用新型软分组法,在多个方面调整等级组合。首先,这种方法依赖于同步性差异性计量,指出对于时间序列数据而言,功能空间是所有时间点的设定。然后,该方法的“软”部分通过使用环形计量来强化同步性信号。最后,我们添加了一种不稳定性通缩缩算法,以删除最不同步的“noisy”数据,从而改进大多数和不太同步的组合的细微组合的分数。因此,该方法将部门-区域数据分为三个组:同步周期投资周期的同步性投资周期可能形成欧盟的同步性周期。