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Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition

Author

Listed:
  • Saulius Jokubaitis

    (Vilnius University
    Vilnius University)

  • Dmitrij Celov

    (Vilnius University
    Vilnius University)

Abstract

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 synchronization phenomenon and 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 measure, 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 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 sectoral-regional 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, suggesting that France, Germany, Austria and Italy, together with export-oriented economic activities, drive the core EU business cycle. The less synchronous group consists of agriculture, public services, and financial services that respond to global shocks to a lesser extent and are more resilient to the COVID-19 outbreak. Finally, the dropout segment includes periphery regions, containing mainly agriculture and other domestically supplied services. The coherence analysis demonstrates the spillover direction, going from the core to other groups.

Suggested Citation

  • Saulius Jokubaitis & Dmitrij Celov, 2023. "Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(3), pages 311-371, November.
  • Handle: RePEc:spr:jbuscr:v:19:y:2023:i:3:d:10.1007_s41549-023-00090-4
    DOI: 10.1007/s41549-023-00090-4
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    Keywords

    Business cycle; Coherence; Core-periphery; European Union; Sectoral-regional; Soft-clustering; Silhouette; Synchronization; Wavelets;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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