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On policymakers’ loss functions 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, 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.

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Bibliographic Info

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 119 (2013)
Issue (Month): 1 ()
Pages: 1-7

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Handle: RePEc:eee:ecolet:v:119:y:2013:i:1:p:1-7

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Web page: http://www.elsevier.com/locate/ecolet

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Keywords: Early warning systems; Policymakers’ loss functions; Policymakers’ preferences; Misclassification costs;

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References

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  1. �scar Jord� & Moritz Schularick & Alan M Taylor, 2011. "Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons," IMF Economic Review, Palgrave Macmillan, vol. 59(2), pages 340-378, June.
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  4. Sarlin, Peter & Peltonen, Tuomas A., 2011. "Mapping the state of financial stability," Working Paper Series 1382, European Central Bank.
  5. 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.
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  7. 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.
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  11. 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.
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  14. Òscar Jordà & Alan M. Taylor, 2011. "Performance Evaluation of Zero Net-Investment Strategies," NBER Working Papers 17150, National Bureau of Economic Research, Inc.
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Cited by:
  1. 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.
  2. Sarlin, Peter & Peltonen, Tuomas A., 2011. "Mapping the state of financial stability," Working Paper Series 1382, European Central Bank.
  3. Betz, Frank & Oprica, Silviu & Peltonen, Tuomas A. & Sarlin, Peter, 2013. "Predicting distress in European banks," Working Paper Series 1597, European Central Bank.
  4. Samuel R\"onnqvist & Peter Sarlin, 2014. "Bank Networks from Text: Interrelations, Centrality and Determinants," Papers 1406.7752, arXiv.org.
  5. 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.

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