JEFMS Journal

Google Scholar

Article Pdf

---

ABSTRACT:

The relevance of Macroeconomic Imbalance Procedure (MIP) Scoreboard indicators in predicting financial crisis risk is explored in this study, with particular attention paid to how they relate to the Country-Level Index of Financial Stress (CLIFS). We apply Random Forest and Gradient Boosted Trees models in TIBCO Statistica to data from 2002 to 2021, providing a novel method of research by combining country-level financial stress indices with indicators of internal and external imbalances. The findings demonstrate that while Gradient Boosted Trees provide better predictive performance, Random Forest offers steady error rates with less overfitting. However, the latter tend to underestimate extreme values and overestimate small ones, highlighting areas for improvement. This paper's novelty lies in its interdependent analysis of these indicators, presenting a new framework for studying macroeconomic imbalances and crisis risks. The findings suggest that additional factors influencing CLIFS remain unexplored, providing avenues for further research to refine predictive models for financial crises.

KEYWORDS:

CLIFS, MIP Scoreboard, Financial Stability, Machine Learning, Random Forest, Gradient Boosted Trees, Economic Modeling, European Union

REFERENCES:

1) Glenn, S. Decision Tree vs Random Forest vs Gradient Boosting Machines: Explained Simply https://www.datasciencecentral.com/decision-tree-vs-random-forest-vs-boosted-trees-explained/

2) Cao, Y. (2024). Prediction and analysis of corporate financial distress based on Random Forest model and GBDT. SHS Web of Conferences, 188, 01001. https://doi.org/10.1051/shsconf/202418801001

3) Abiad, Abdul, (2003), Early Warning Systems: A Survey and a Regime-Switching Approach, No 2003/032, IMF Working Papers, International Monetary Fund, https://EconPapers.repec.org/RePEc:imf:imfwpa:2003/032

4) Alessi, L., Detken, C. (2018), “Identifying excessive credit growth and leverage”, Journal of Financial Stability, Elsevier, Vol. 35, pp. 215–225, doi: 10.1016/J.JFS.2017.06.005.

5) Bobeva, D.N., Atanasov, A. (2016), “Macroeconomic Imbalances in Euro- and Non-Euro Area Member States”, SSRN Electronic Journal, Elsevier BV, doi: 10.2139/SSRN.2818985.

6) Breiman, L. (2001), “Random forests”, Machine Learning, Springer, Vol. 45 No. 1, pp. 5–32, doi: 10.1023/A:1010933404324/METRICS.

7) Casabianca, E.J., Catalano, M., Forni, L., Giarda, E., Passeri, S. (2022), “A machine learning approach to rank the determinants of banking crises over time and across countries”, Journal of International Money and Finance, Pergamon, Vol. 129, p. 102739, doi: 10.1016/J.JIMONFIN.2022.102739.

8) Christofides C., Eicher T., Papageorgiou C.(2016),”Did established Early Warning Signals predict the 2008 crises?, European Economic Review, Volume 81, 2016, Pages 103-114, ISSN 0014-2921, https://doi.org/10.1016/j.euroecorev.2015.04.004, https://www.sciencedirect.com/science/article/pii/S0014292115000604

9) Dany-Knedlik, G., Kämpfe, M., Knedlik, T. (2021), “The appropriateness of the macroeconomic imbalance procedure for Central and Eastern European Countries”, Empirica, Springer, Vol. 48 No. 1, pp. 123–139, doi: 10.1007/S10663-020-09471-9/TABLES/5.

10) Erhart, S. (2019), “Exposition, Climax, Denouement: Life-Cycle Evaluation of the Recent Financial Crisis in the EU by Linking the ESRB Financial Crisis Database to the European Commission’s Macroeconomic Imbalance Procedure Scoreboard”, SSRN Electronic Journal, Elsevier BV, doi: 10.2139/SSRN.3723438.

11) Frankel, J.A., Saravelos, G. (2010), “Are Leading Indicators of Financial Crises Useful for Assessing Country Vulnerability? Evidence from the 2008-09 Global Crisis”, Cambridge, MA, doi: 10.3386/W16047.

12) Frankel, J.A., Wei, S.-J. (2004), “Managing Macroeconomic Crises”, Cambridge, MA, doi: 10.3386/W10907.

13) Gadea Rivas, M.D. and Perez-Quiros, G. (2015), “The Failure to Predict the Great Recession—A View Through the Role of Credit”, Journal of the European Economic Association, Oxford Academic, Vol. 13 No. 3, pp. 534–559, doi: 10.1111/JEEA.12122.

14) Gros, D., Busse, M. (2013), “The Macroeconomic Imbalance Procedure and Germany: When is a Current Account Surplus an ‘Imbalance’?”, 13 November.

15) Gu, X., Mamon, R., Duprey, T. (2024), “Interfacing learning methods for anomaly detection in multi-country financial stress indicators”, Knowledge-Based Systems, Elsevier, Vol. 294, p. 111712, doi: 10.1016/J.KNOSYS.2024.111712.

16) Hamilton, J. D., (1989), “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, vol. 57, no. 2, pp. 357–84. JSTOR, https://doi.org/10.2307/1912559. Accessed 18 Oct. 2024.

17) Knedlik, T., Scheufele, R., (2008), FORECASTING CURRENCY CRISES: WHICH METHODS SIGNALED THE SOUTH AFRICAN CRISIS OF JUNE 2006?, South African Journal of Economics, 76, issue 3, p. 367-383, https://EconPapers.repec.org/RePEc:bla:sajeco:v:76:y:2008:i:3:p:367-383.

18) Krkoska, L., (2001), Assessing Macroeconomic Vulnerability in Central Europe, Post-Communist Economies, 13, issue 1, p. 41-55, https://EconPapers.repec.org/RePEc:taf:pocoec:v:13:y:2001:i:1:p:41-55

19) Martínez-Jaramillo, S., Montañez-Enríquez, R., Busch, M.O., Ramos-Francia, M., Rodríguez-Martínez, A., Manuel Sánchez-Martínez, J. (2022), “Stress-ridden Finance and Growth Losses: Does Financial Development Break the Link?”

20) Nag, Ashok & Mitra, Amit. (1999), “Neural networks and early warning indicators of currency crisis, Reserve Bank of India Occasional Papers. 20. 183-222.

21) Ostrihoň, F. (2022), “Exploring macroeconomic imbalances through EU Alert Mechanism Reports”, European Journal of Political Economy, North-Holland, Vol. 75, p. 102188, doi: 10.1016/J.EJPOLECO.2022.102188.

22) Peltonen, Tuomas A., (2006). "Are emerging market currency crises predictable? A test," Working Paper Series 571, European Central Bank.

23) Reinhart, C. M., Reinhart, V. R. (1998), "Some Lessons for Policy Makers Who Deal with the Mixed Blessing of Capital Inflows". Capital Flows and Financial Crises, edited by Miles Kahler, Ithaca, NY: Cornell University Press, 1998, pp. 93-127. https://doi.org/10.7591/9781501731402-007

24) Ristolainen K., (2018), "Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity," Scandinavian Journal of Economics, Wiley Blackwell, vol. 120(1), pages 31-62, January.

25) Schumacher, L., Bléjer, M., (1998), Central Bank Vulnerability and the Credibility of Commitments: A Value-at-Risk Approach to Currency Crises, No 1998/065, IMF Working Papers, International Monetary Fund, https://EconPapers.repec.org/RePEc:imf:imfwpa:1998/065.

26) “The Macroeconomic Imbalance Procedure (MIP) introduced - Statistics Explained”. (n.d.). , available at: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=The_Macroeconomic_Imbalance_Procedure_(MIP)_introduced (accessed 11 October 2024).

27) Torój, A. (2017), “Managing external macroeconomic imbalances in the EU: the welfare cost of scoreboard-based constraints”, Economic Modelling, North-Holland, Vol. 61, pp. 293–311, doi: 10.1016/J.ECONMOD.2016.10.009.