IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v119y2013i1p1-7.html
   My bibliography  Save this article

On policymakers’ loss functions and the evaluation of early warning systems

Author

Listed:
  • Sarlin, Peter

Abstract

This paper introduces a new loss function and Usefulness measure for evaluating early warning systems (EWSs) that incorporate policymakers’ preferences between issuing false alarms and missing crises, and individual observations. The novelty derives from three enhancements: (i) accounting for unconditional probabilities of the classes, (ii) computing the proportion of available Usefulness that the model captures, and (iii) weighting observations by their importance for the policymaker. The proposed measures are model free such that they can be used to assess early warning signals issued by any type of EWS, and flexible for any type of crisis. Applications to two renowned EWSs, and comparisons to two common evaluation measures, illustrate the importance of an objective criterion for choosing a final specification and threshold value, and for models to be useful, the need to be more concerned about the rare class and the importance of correctly classifying observations of most relevant entities.

Suggested Citation

  • Sarlin, Peter, 2013. "On policymakers’ loss functions and the evaluation of early warning systems," Economics Letters, Elsevier, vol. 119(1), pages 1-7.
  • Handle: RePEc:eee:ecolet:v:119:y:2013:i:1:p:1-7
    DOI: 10.1016/j.econlet.2012.12.030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176513000025
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2012.12.030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    2. 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.
    3. Mr. Kasper Lund-Jensen, 2012. "Monitoring Systemic Risk Basedon Dynamic Thresholds," IMF Working Papers 2012/159, International Monetary Fund.
    4. A. Berg & C. Pattillo, 1999. "What Caused the Asian Crises: An Early Warning System Approach," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 28(3), pages 285-334, November.
    5. Bertrand Candelon & Elena-Ivona Dumitrescu & Christophe Hurlin, 2012. "How to Evaluate an Early-Warning System: Toward a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(1), pages 75-113, April.
    6. Sarlin, Peter & Peltonen, Tuomas A., 2013. "Mapping the state of financial stability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 46-76.
    7. Alessi, Lucia & Detken, Carsten, 2011. "Quasi real time early warning indicators for costly asset price boom/bust cycles: A role for global liquidity," European Journal of Political Economy, Elsevier, vol. 27(3), pages 520-533, September.
    8. Òscar Jordà & Moritz Schularick & Alan M Taylor, 2011. "Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 59(2), pages 340-378, June.
    9. Abhyankar, Abhay & Sarno, Lucio & Valente, Giorgio, 2005. "Exchange rates and fundamentals: evidence on the economic value of predictability," Journal of International Economics, Elsevier, vol. 66(2), pages 325-348, July.
    10. Mr. Axel Schimmelpfennig & Nouriel Roubini & Paolo Manasse, 2003. "Predicting Sovereign Debt Crises," IMF Working Papers 2003/221, International Monetary Fund.
    11. Fuertes, Ana-Maria & Kalotychou, Elena, 2006. "Early warning systems for sovereign debt crises: The role of heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1420-1441, November.
    12. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "Optimal design of early warning systems for sovereign debt crises," International Journal of Forecasting, Elsevier, vol. 23(1), pages 85-100.
    13. Òscar Jordà & Alan M. Taylor, 2011. "Performance Evaluation of Zero Net-Investment Strategies," NBER Working Papers 17150, National Bureau of Economic Research, Inc.
    14. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    15. Bussiere, Matthieu & Fratzscher, Marcel, 2008. "Low probability, high impact: Policy making and extreme events," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 111-121.
    16. Bertrand Candelon & Elena-Ivona Dumitrescu & Christophe Hurlin, 2012. "How to evaluate an Early Warning System ?," Working Papers halshs-00450050, HAL.
    17. Berg, Andrew & Pattillo, Catherine, 1999. "Predicting currency crises:: The indicators approach and an alternative," Journal of International Money and Finance, Elsevier, vol. 18(4), pages 561-586, August.
    18. repec:zbw:bofitp:2011_018 is not listed on IDEAS
    19. Demirguc, Asli & Detragiache, Enrica, 2000. "Monitoring Banking Sector Fragility: A Multivariate Logit Approach," The World Bank Economic Review, World Bank Group, vol. 14(2), pages 287-307, May.
    20. Mathias Drehmann & Claudio Borio & Kostas Tsatsaronis, 2011. "Anchoring Countercyclical Capital Buffers: The role of Credit Aggregates," International Journal of Central Banking, International Journal of Central Banking, vol. 7(4), pages 189-240, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Betz, Frank & Oprică, Silviu & Peltonen, Tuomas A. & Sarlin, Peter, 2014. "Predicting distress in European banks," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 225-241.
    2. Kauko, Karlo, 2014. "How to foresee banking crises? A survey of the empirical literature," Economic Systems, Elsevier, vol. 38(3), pages 289-308.
    3. 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.
    4. Sondermann, David & Zorell, Nico, 2019. "A macroeconomic vulnerability model for the euro area," Working Paper Series 2306, European Central Bank.
    5. Sarlin, Peter & von Schweinitz, Gregor, 2021. "Optimizing Policymakers’ Loss Functions In Crisis Prediction: Before, Within Or After?," Macroeconomic Dynamics, Cambridge University Press, vol. 25(1), pages 100-123, January.
    6. von Schweinitz, Gregor & Sarlin, Peter, 2015. "Signaling Crises: How to Get Good Out-of-Sample Performance Out of the Early Warning System," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112964, Verein für Socialpolitik / German Economic Association.
    7. Roberto Savona & Marika Vezzoli, 2015. "Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 66-92, February.
    8. Peter Sarlin & Dorina Marghescu, 2011. "Neuro‐Genetic Predictions Of Currency Crises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(4), pages 145-160, October.
    9. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    10. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2018. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," Discussion Papers 48/2018, Deutsche Bundesbank.
    11. Lo Duca, Marco & Koban, Anne & Basten, Marisa & Bengtsson, Elias & Klaus, Benjamin & Kusmierczyk, Piotr & Lang, Jan Hannes & Detken, Carsten & Peltonen, Tuomas, 2017. "A new database for financial crises in European countries," ESRB Occasional Paper Series 13, European Systemic Risk Board.
    12. Hamdaoui, Mekki, 2016. "Are systemic banking crises in developed and developing countries predictable?," Journal of Multinational Financial Management, Elsevier, vol. 37, pages 114-138.
    13. Ari, Ali, 2012. "Early warning systems for currency crises: The Turkish case," Economic Systems, Elsevier, vol. 36(3), pages 391-410.
    14. Panayotis Michaelides & Mike Tsionas & Panos Xidonas, 2020. "A Bayesian Signals Approach for the Detection of Crises," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 551-585, September.
    15. Roy, Saktinil & Kemme, David M., 2012. "Causes of banking crises: Deregulation, credit booms and asset bubbles, then and now," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 270-294.
    16. Honda, Jiro & Tapsoba, René & Issifou, Ismael, 2022. "When do we repair the roof? Insights from responses to fiscal crisis early warning signals," International Economics, Elsevier, vol. 172(C), pages 349-367.
    17. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    18. Mikkel Hermansen & Oliver Röhn, 2017. "Economic resilience: The usefulness of early warning indicators in OECD countries," OECD Journal: Economic Studies, OECD Publishing, vol. 2016(1), pages 9-35.
    19. Barbara Jarmulska, 2022. "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 455-490, April.
    20. Tjeerd M. Boonman & Jan P. A. M. Jacobs & Gerard H. Kuper & Alberto Romero, 2019. "Early Warning Systems for Currency Crises with Real-Time Data," Open Economies Review, Springer, vol. 30(4), pages 813-835, September.

    More about this item

    Keywords

    Early warning systems; Policymakers’ loss functions; Policymakers’ preferences; Misclassification costs;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • F01 - International Economics - - General - - - Global Outlook
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G01 - Financial Economics - - General - - - Financial Crises

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:119:y:2013:i:1:p:1-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.