Saulius Jokubaitis, Dmitrij Celov
arXiv 28 Jun 2022 · Econometrics · publishedJournal of Business Cycle Research (2023) · 1 citations (OpenAlex)
arXiv:2206.14128 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_command · 90% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Celov, D. and M. Comunale (2021) Advances in Econometrics self | 1.000 | 6 | 3 | 100% |
| 2 | Ahlborn, M. and M. Wortmann (2018) The core–periphery pattern of European business cycles: A fuzzy clustering approach | 0.928 | 4 | 3 | 100% |
| 3 | Coudert, V., C. Couharde, C. Grekou, and V. Mignon (2020) Heterogeneity within the euro area: New insights into an old story | 0.811 | 4 | 2 | 100% |
| 4 | Bunyan, S., D. Duffy, G. Filis, and I. Tingbani (2020) Fiscal policy, government size and EMU business cycle synchronization | 0.737 | 3 | 2 | 100% |
| 5 | Inklaar, R., R. Jong-A-Pin, and J. De Haan (2008) Trade and business cycle synchronization in OECD countries—a re-examination | 0.644 | 2 | 2 | 100% |
| 6 | Mink, M., J. P. Jacobs, and J. de Haan (2012) Measuring coherence of output gaps with an application to the euro area | 0.644 | 2 | 2 | 100% |
| 7 | Crowley, P. M. and D. G. Mayes (2009) How fused is the euro area core?: An evaluation of growth cycle co-movement and synchronization using wavelet analysis | 0.585 | 3 | 1 | 100% |
| 8 | Artis, M. J. and W. Zhang (2002) Membership of EMU: A fuzzy clustering analysis of alternative criteria | 0.511 | 2 | 1 | 100% |
| 9 | Bengoechea, P., M. Camacho, and G. Perez-Quiros (2006) A useful tool for forecasting the euro-area business cycle phases | 0.511 | 2 | 1 | 100% |
| 10 | Campos, N. F. and C. Macchiarelli (2016) Core and periphery in the European Monetary Union: Bayoumi and Eichengreen 25 years later | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 44 scored citations.