B1999
Title: A soft-clustering approach for regional-sectoral EU business cycle synchronization
Authors: Saulius Jokubaitis - Vilnius University (Lithuania) [presenting]
Dmitrij Celov - Vilnius University (Lithuania)
Abstract: The focus is on the regional-sectoral view of the business cycle synchronization in the EU -- a necessary condition for the optimal currency area. 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 a soft-clustering approach, which adjusts hierarchical clustering in several aspects. First, the method relies on synchronicity dissimilarity measure, noting that, for time series data, the feature space is the set of all points in time. The ``soft'' part of the approach strengthens the synchronization signal by using silhouette scores. 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 splits the regional-sectoral data into three groups: the synchronous group that shapes the core EU business cycle; the less synchronous group that may hint at lagging sectors and regions; the asynchronous noisy group that may help investors to diversify through-the-cycle risks of their investment portfolios. Our results do not contradict the core-periphery hypothesis and provide additional evidence due to the added granularity of the regional-sectoral composition.
