论文标题
欧盟中的商业周期同步：通过软簇和小波分解的区域部门外观
Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition
论文作者
论文摘要
本文详细阐述了欧盟商业周期同步的部门区域视图，这是最佳货币领域的必要条件。我们认为，数据的完整聚集可以提高决策者对商业周期的理解，并扩展到经济决策的质量。我们通过应用小波方法来定义业务周期，以超过2000q1至2021q2的漂移调整的总增值数据。为了应用同步分析，我们提出了新型的软聚类方法，该方法在几个方面调整了层次聚类。首先，该方法取决于同步性差异度量，并指出，对于时间序列数据，特征空间是所有时间点的集合。然后，方法的``软''部分通过使用轮廓测量方法来增强同步信号。最后，我们添加了一种概率的稀疏算法，以删除最异步的``嘈杂''数据，以改善最多和较少同步组的轮廓分数。因此，该方法将部门区域数据分为三组：塑造欧盟商业周期的同步组；可能暗示周期预测相关信息的较不同步组；可能会帮助投资者多样化投资组合的整个周期风险的异步集团。结果支持核心周期假设。
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.
