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Predicting financial crises: The (statistical) significance of the signals approach

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  • El-Shagi, M.
  • Knedlik, T.
  • von Schweinitz, G.

Abstract

The signals approach as an early-warning system has been fairly successful in detecting crises, but it has so far failed to gain popularity in the scientific community because it cannot distinguish between randomly achieved in-sample fit and true predictive power. To overcome this obstacle, we test the null hypothesis of no correlation between indicators and crisis probability in three applications of the signals approach to different crisis types. To that end, we propose bootstraps specifically tailored to the characteristics of the respective datasets. We find (1) that previous applications of the signals approach yield economically meaningful results; (2) that composite indicators aggregating information contained in individual indicators add value to the signals approach; and (3) that indicators which are found to be significant in-sample usually perform similarly well out-of-sample.

Suggested Citation

  • El-Shagi, M. & Knedlik, T. & von Schweinitz, G., 2013. "Predicting financial crises: The (statistical) significance of the signals approach," Journal of International Money and Finance, Elsevier, vol. 35(C), pages 76-103.
  • Handle: RePEc:eee:jimfin:v:35:y:2013:i:c:p:76-103
    DOI: 10.1016/j.jimonfin.2013.02.001
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    References listed on IDEAS

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    Cited by:

    1. Sarlin, Peter, 2013. "On policymakers’ loss functions and the evaluation of early warning systems," Economics Letters, Elsevier, vol. 119(1), pages 1-7.
    2. Tobias Knedlik & Gregor Von Schweinitz, 2012. "Macroeconomic Imbalances as Indicators for Debt Crises in Europe," Journal of Common Market Studies, Wiley Blackwell, vol. 50(5), pages 726-745, September.
    3. Tristan Nguyen & Nguyen Ngoc Duy, 2017. "Developing an Early Warning System for Financial Crises in Vietnam," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 7(4), pages 413-430, April.
    4. Yamarik, Steven & El-Shagi, Makram & Yamashiro, Guy, 2016. "Does inequality lead to credit growth? Testing the Rajan hypothesis using state-level data," Economics Letters, Elsevier, vol. 148(C), pages 63-67.
    5. Hyeongwoo Kim & Kyunghwan Ko, 2017. "Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach," Working Papers 2017-14, Economic Research Institute, Bank of Korea.
    6. Hyeongwoo Kim & Hyun Hak Kim & Wen Shi, 2015. "Forecasting Financial Stress Indices in Korea: A Factor Model Approach," Working Papers 2015-30, Economic Research Institute, Bank of Korea.
    7. Makram El-shagi & Logan J Kelly, 2014. "Liquidity in the liquidity crisis: evidence from Divisia monetary aggregates in Germany and the European crisis countries," Economics Bulletin, AccessEcon, vol. 34(1), pages 63-72.
    8. Knedlik, Tobias, 2012. "The European Commission’s Scoreboard of Macroeconomic Imbalances – The Impact of Preferences on an Early Warning System," IWH Discussion Papers 10/2012, Halle Institute for Economic Research (IWH).
    9. Hyeongwoo Kim & Kyunghwan Ko, 2017. "Improving Forecast Accuracy of Financial Vulnerability: PLS Factor Model Approach," Auburn Economics Working Paper Series auwp2017-03, Department of Economics, Auburn University.
    10. Zhi-Qiang Jiang & Gang-Jin Wang & Askery Canabarro & Boris Podobnik & Chi Xie & H. Eugene Stanley & Wei-Xing Zhou, 2016. "Short term prediction of extreme returns based on the recurrence interval analysis," Papers 1610.08230, arXiv.org.
    11. Knedlik, Tobias, 2014. "The impact of preferences on early warning systems — The case of the European Commission's Scoreboard," European Journal of Political Economy, Elsevier, vol. 34(C), pages 157-166.
    12. Sarlin, Peter & von Schweinitz, Gregor, 2015. "Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?," IWH Discussion Papers 6/2015, Halle Institute for Economic Research (IWH).
    13. repec:bla:rdevec:v:21:y:2017:i:3:p:451-474 is not listed on IDEAS
    14. Christian Dreger & Konstantin A. Kholodilin, 2018. "Early Warning System of Government Debt Crises," Discussion Papers of DIW Berlin 1724, DIW Berlin, German Institute for Economic Research.
    15. Makram El-Shagi & Axel Lindner & Gregor von Schweinitz, 2016. "Real Effective Exchange Rate Misalignment in the Euro Area: A Counterfactual Analysis," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 37-66, February.
    16. repec:eee:ecmode:v:67:y:2017:i:c:p:275-284 is not listed on IDEAS
    17. Tomáš Domonkos & Filip Ostrihoň & Ivana Šikulová & Maria Širaňová, 2016. "Analyzing macroeconomic imbalances in the EU," EcoMod2016 9660, EcoMod.
    18. Hyeongwoo Kim & Wen Shi, 2015. "Forecasting Financial Market Vulnerability in the U.S.: A Factor Model Approach," Auburn Economics Working Paper Series auwp2015-04, Department of Economics, Auburn University.
    19. Kämpfe, Martina & Knedlik, Tobias, 2017. "The appropriateness of the macroeconomic imbalance procedure for Central and Eastern European countries," IWH Discussion Papers 16/2017, Halle Institute for Economic Research (IWH).
    20. von Schweinitz, Gregor & Sarlin, Peter, 2015. "Signaling Crises: How to Get Good Out-of-Sample Performance Out of the Early Warning System," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112964, Verein für Socialpolitik / German Economic Association.
    21. Hyeongwoo Kim & Wen Shi, 2016. "Forecasting Financial Vulnerability in the US: A Factor Model Approach," Auburn Economics Working Paper Series auwp2016-15, Department of Economics, Auburn University.
    22. Markus Holopainen & Peter Sarlin, 2015. "Toward robust early-warning models: A horse race, ensembles and model uncertainty," Papers 1501.04682, arXiv.org, revised Apr 2016.

    More about this item

    Keywords

    Early warning system; Signals approach; Bootstrap;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General
    • F01 - International Economics - - General - - - Global Outlook

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