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Business cycle synchronization between the EU and Western Balkan candidate economies: A Wavelet Analysis

Petar Jolakoski, Viktor Stojkoski, Dragan Tevdovski

Abstract

Business cycle synchronization between EU and Western Balkan candidate economies is usually modeled with aggregate time-domain correlations that mix short-run and long-run dynamics. This paper addresses that limitation by combining wavelet-based time-frequency decomposition with Bayesian zero-inflated beta regression. Using annual dyad-year data for 2001--2021, we estimate synchronization separately at shorter (1.5--4.5 years) and longer (4.5--8.5 years) horizons and relate each horizon to its correlates. The results show that EU--WB dyads are less synchronized than EU--EU dyads in the short run, and that trade deepening over time is more positively associated with short-run synchronization in EU--WB pairs. At longer horizons, the positive association between shared EU/EMU membership and synchronization weakens or reverses when the same country pair moves into deeper institutional integration, while differences across country pairs in average EU/EMU status become negligible. Over the same horizon, trade deepening within a pair is more consistently associated with synchronization, and more persistent structural dissimilarity is associated with lower synchronization. EU--WB dyads are no longer clearly less synchronized at these frequencies, and the remaining convergence pattern is more consistent with sectoral differences narrowing over time than with trade. These findings indicate that synchronization channels are horizon-dependent and that conclusions based on single-horizon correlation measures can obscure the distinction between short-term coupling and structural convergence.

Business cycle synchronization between the EU and Western Balkan candidate economies: A Wavelet Analysis

Abstract

Business cycle synchronization between EU and Western Balkan candidate economies is usually modeled with aggregate time-domain correlations that mix short-run and long-run dynamics. This paper addresses that limitation by combining wavelet-based time-frequency decomposition with Bayesian zero-inflated beta regression. Using annual dyad-year data for 2001--2021, we estimate synchronization separately at shorter (1.5--4.5 years) and longer (4.5--8.5 years) horizons and relate each horizon to its correlates. The results show that EU--WB dyads are less synchronized than EU--EU dyads in the short run, and that trade deepening over time is more positively associated with short-run synchronization in EU--WB pairs. At longer horizons, the positive association between shared EU/EMU membership and synchronization weakens or reverses when the same country pair moves into deeper institutional integration, while differences across country pairs in average EU/EMU status become negligible. Over the same horizon, trade deepening within a pair is more consistently associated with synchronization, and more persistent structural dissimilarity is associated with lower synchronization. EU--WB dyads are no longer clearly less synchronized at these frequencies, and the remaining convergence pattern is more consistent with sectoral differences narrowing over time than with trade. These findings indicate that synchronization channels are horizon-dependent and that conclusions based on single-horizon correlation measures can obscure the distinction between short-term coupling and structural convergence.

Paper Structure

This paper contains 17 sections, 19 equations, 1 figure, 12 tables.

Table of Contents

  1. Introduction
  2. Western Balkans Road to Business Cycle Integration with the EU
  3. Data
  4. Methodology
  5. Wavelet Coherence: Measuring Co-movement Across Frequencies
  6. Constructing the Dependent Variable: Annual In-Phase Synchronization
  7. Econometric model: Zero-Inflated Beta Regression
  8. Results
  9. Wavelet evidence of synchronization
  10. Determinants of short-run synchronization
  11. Determinants of long-run synchronization
  12. Western Balkans convergence analysis
  13. Conclusion
  14. Wavelet Analysis: Mathematical Details
  15. Data Construction
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Wavelet coherence and phase dynamics relative to the EU aggregate. Each row corresponds to a country pair (SVN, MKD, MNE, SRB versus EU weighted average). Left column: coherence (color scale 0--1; warmer colors indicate higher coherence) with a dashed contour for $p \le 0.05$ and the cone of influence (COI) shaded in light gray; arrows encode phase differences. Middle and right columns: phase differences for the 1.5--4.5 and 4.5--8.5 year bands; the secondary axis overlays $\Delta T$ (gray) and reliable $\Delta T^*$ (red).