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On policymakers' loss function and the evaluation of early warning systems

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  • 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, as well as 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 signals issued by any type of EWS, such as logit and probit analysis and the signaling approach, and flexible for any type of crisis EWSs, such as banking, debt and currency crises. Applications to two renowned EWSs, and comparisons to two commonly used evaluation measures, illustrate three key implications of the new measures: i) further highlights the importance of an objective criterion for choosing a final specification and threshold value, and for models to be useful ii) the need to be more concerned about the rare class and iii) the importance of correctly classifying observations of the most relevant entities. Beyond financial stability surveillance, this paper also opens the door for cost-sensitive evaluations of predictive models in other tasks. JEL Classification: E44, E58, F01, F37, G01

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 1509.

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Date of creation: Feb 2013
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Handle: RePEc:ecb:ecbwps:20131509

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Keywords: Early warning systems; misclassification costs; policymakers' preferences;

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  1. Sarlin, Peter & Peltonen, Tuomas A., 2011. "Mapping the state of financial stability," Working Paper Series 1382, European Central Bank.
  2. Mathias Drehmann & Claudio Borio & Kostas Tsatsaronis, 2011. "Anchoring countercyclical capital buffers: the role of credit aggregates," BIS Working Papers 355, Bank for International Settlements.
  3. Axel Schimmelpfennig & Nouriel Roubini & Paolo Manasse, 2003. "Predicting Sovereign Debt Crises," IMF Working Papers 03/221, International Monetary Fund.
  4. Reinhart, Carmen & Kaminsky, Graciela & Lizondo, Saul, 1998. "Leading Indicators of Currency Crises," MPRA Paper 6981, University Library of Munich, Germany.
  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, vol. 60(1), pages 75-113, April.
  6. Òscar Jordà & Alan M. Taylor, 2011. "Performance Evaluation of Zero Net-Investment Strategies," NBER Working Papers 17150, National Bureau of Economic Research, Inc.
  7. Makram El-Shagi & Tobias Knedlik & Gregor von Schweinitz, 2012. "Predicting Financial Crises: The (Statistical) Significance of the Signals Approach," IWH Discussion Papers 3, Halle Institute for Economic Research.
  8. Kasper Lund-Jensen, 2012. "Monitoring Systemic Risk Basedon Dynamic Thresholds," IMF Working Papers 12/159, International Monetary Fund.
  9. 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.
  10. 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.
  11. 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.
  12. Giorgio Valente & Lucio Sarno & Abhay Abhayankar, 2004. "Exchange Rates and Fundamentals: Evidence on the Economic Value of Predictability," Working Papers wp04-01, Warwick Business School, Finance Group.
  13. Òscar Jordà & Moritz Schularick & Alan M. Taylor, 2010. "Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons," NBER Working Papers 16567, National Bureau of Economic Research, Inc.
  14. 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.
  15. Demirguc, Asli & Detragiache, Enrica, 2000. "Monitoring Banking Sector Fragility: A Multivariate Logit Approach," World Bank Economic Review, World Bank Group, vol. 14(2), pages 287-307, May.
  16. 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.
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Cited by:
  1. Behn, Markus & Detken, Carsten & Peltonen, Tuomas A. & Schudel, Willem, 2013. "Setting countercyclical capital buffers based on early warning models: would it work?," Working Paper Series 1604, European Central Bank.
  2. Sarlin, Peter & Peltonen, Tuomas A., 2011. "Mapping the state of financial stability," Working Paper Series 1382, European Central Bank.
  3. Samuel R\"onnqvist & Peter Sarlin, 2014. "Bank Networks from Text: Interrelations, Centrality and Determinants," Papers 1406.7752, arXiv.org.
  4. Betz, Frank & Oprica, Silviu & Peltonen, Tuomas A. & Sarlin, Peter, 2013. "Predicting distress in European banks," Working Paper Series 1597, European Central Bank.
  5. Orsolya Csortos & Zoltán Szalai, 2013. "Assessment of macroeconomic imbalance indicators," MNB Bulletin, Magyar Nemzeti Bank (the central bank of Hungary), vol. 8(3), pages 14-24, October.

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