Abstract
Do countries with similar macroeconomic dynamics during pre-crisis times experience a common subsequent crisis-, respectively non-crisis-, status? Based on the Euclidean distance, a community structure detection algorithm generates the network topology of four distinct pre-crisis periods between 1990 and 2008 comprising 27 countries. The desired outcome is a clear-cut separation of future crisis from non-crisis economies. The approach succeeds in uncovering prominent cluster formations, whereas period-specific heatmaps reveal the time-varying importance of the considered indicators. The heterogeneous cluster-formation does not allow to infer any dynamics, which would unambiguously hint at an upcoming crisis event.
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Notes
- 1.
See Sect. 2.3 for an explanation of the modularity algorithm and the measure \(Q^G\).
- 2.
The size of the threshold for the largest distances equals the one for the shortest distances.
- 3.
See Table 1 for the country- and period-specific flagging.
- 4.
We tried to stick to \(q \in \left[ -4;4\right] \), i.e. keep the intervals of crisis-occurrences rather tight in order to not veil the existence of time-specific dynamics. However, this interval can be changed arbitrarily.
- 5.
EX = Exports; Reserves = International Reserves; R_ExchRate_DevTrend = Real Exchange Rate Overvaluation relative to Trend; N_STDebt/Reserves = Nominal Short-Term Debt to International Reserves; CA/GDP = Current Account Deficit relative to GDP.
- 6.
The raw-data reveals indeed a Current Account Deficit.
- 7.
Cross-checking the raw-data reveals indeed a Current Account Deficit.
- 8.
See Fig. 1d Clusters 3 and 4.
- 9.
See Fig. 1d Clusters 0 and 1.
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Göbel, M., Araújo, T. (2020). A Network Structure Analysis of Economic Crises. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_44
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