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Economic resilience: The usefulness of early warning indicators in OECD countries

Author

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  • Mikkel Hermansen

    (OECD)

  • Oliver Röhn

    (OECD)

Abstract

The global financial crisis and the high associated costs have revived the academic and policy interest in “early warning indicators” of crises. This paper provides empirical evidence on the usefulness of a new set of vulnerability indicators, proposed in a companion paper (Röhn et al., 2015), in predicting severe recessions and crises in OECD countries. To evaluate the usefulness of the indicators the signalling approach is employed, which takes into account policy makers’ preferences between missing crises and false alarms. Our empirical evidence shows that the majority of indicators would have helped to predict severe recessions in the 34 OECD economies and Latvia between 1970 and 2014. Indicators of global risks consistently outperform domestic indicators in terms of their usefulness, highlighting the importance of taking international developments into account when assessing a country’s vulnerabilities. In the domestic areas, indicators that measure asset market imbalances (real house and equity prices, house price-to-income and house price-to-rent ratios), also perform consistently well both in and out-of sample. Domestic credit related variables appear particularly useful in signalling upcoming banking crises and in predicting the global financial crisis out-of-sample. The results are broadly robust to different definitions of costly events, different forecasting horizons and different time and country samples. Résilience économique : L'utilité des indicateurs d'alerte rapide dans des pays de l'OCDE La crise financière mondiale et les coûts associés élevés ont ravivé l'intérêt pour les « indicateurs d'alerte rapide » des crises. Cette étude fournit des données statistiques sur l'utilité d'un nouvel ensemble d'indicateurs de vulnérabilité, proposé dans une étude connexe (Röhn et al., 2015), pour prédire les récessions graves et les crises dans les pays de l'OCDE. Pour évaluer l'utilité des indicateurs la méthode de signalisation est employée. Celle-ci prend en compte les préférences des décideurs politiques entre les crises manquantes et les fausses alarmes. Les résultats de l’analyse statistique montrent que la majorité des indicateurs aurait aidé à prédire les récessions sévères dans les 34 économies de l'OCDE et la Lettonie entre 1970 et 2014. Les indicateurs de risque global surclassent systématiquement les indicateurs domestiques en termes d’information utile, soulignant l'importance de prendre les développements internationaux en compte lors de l'évaluation des vulnérabilités d'un pays. Dans les champs domestiques, des indicateurs qui mesurent les déséquilibres du marché des actifs (les prix réels des logements et le cours des actions, le ratio du prix des logements au revenu disponible et le ratio du prix des logements au coût des loyers), performe bien dans et hors de l'échantillon. Les variables reliées au crédit domestique semblent particulièrement utile dans la signalisation des crises bancaires et à prédire la crise financière mondiale hors-échantillon. Les résultats sont globalement robustes pour différentes définitions d'événements onéreux, différents horizons de prévision et différents échantillons de temps et de pays.

Suggested Citation

  • Mikkel Hermansen & Oliver Röhn, 2015. "Economic resilience: The usefulness of early warning indicators in OECD countries," OECD Economics Department Working Papers 1250, OECD Publishing.
  • Handle: RePEc:oec:ecoaaa:1250-en
    DOI: 10.1787/5jrxhgfqx3mv-en
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    Cited by:

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    3. Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2020. "Crisis transmission: Visualizing vulnerability," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
    4. Rakesh Padhan & K. P. Prabheesh, 2019. "Effectiveness Of Early Warning Models: A Critical Review And New Agenda For Future Direction," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 22(4), pages 457-484, December.
    5. Gould, David M. & Melecky, Martin & Panterov, Georgi, 2016. "Finance, growth and shared prosperity: Beyond credit deepening," Journal of Policy Modeling, Elsevier, vol. 38(4), pages 737-758.
    6. Jollès, Maya & Meyermans, Eric & Vašíček, Bořek, 2023. "Determinants of macroeconomic resilience in the euro area: An empirical assessment of national policy levers," Economic Systems, Elsevier, vol. 47(3).
    7. Sondermann, David & Zorell, Nico, 2019. "A macroeconomic vulnerability model for the euro area," Working Paper Series 2306, European Central Bank.
    8. Maria Chiara Cavalleri & Boris Cournède & Volker Ziemann, 2019. "Housing markets and macroeconomic risks," OECD Economics Department Working Papers 1555, OECD Publishing.
    9. Aslihan Atabek & Dan Andrews & Rauf Gönenç, 2017. "Rebalancing Turkey’s growth by improving resource allocation and productivity in manufacturing," OECD Economics Department Working Papers 1367, OECD Publishing.
    10. Helene Olsen & Harald Wieslander, 2020. "The Impact of Monetary Policy on Leading Variables for Financial Stability in Norway," Working Papers No 02/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. Mr. Plamen K Iossifov, 2021. "Cyclical Patterns of Systemic Risk Metrics: Cross-Country Analysis," IMF Working Papers 2021/028, International Monetary Fund.

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    Keywords

    crise; crisis; déséquilibres; recession; resilience; résilience; vulnerability; vulnérabilité;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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