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A Model to Assess the Probabilities of Growth, Fiscal, and Financial Crises

Author

Listed:
  • Mr. Suman S Basu
  • Mr. Marcos d Chamon
  • Mr. Christopher W. Crowe

Abstract

This paper summarizes a suite of early warning models to assess the probabilities of growth, fiscal, and financial crises in advanced economies and emerging markets. We estimate separate signal-extraction models for each type of crisis and sample of countries, and we use our results to generate “histories of vulnerabilities” for countries, regions, and the world. For the global financial crisis, our models report that vulnerabilities in advanced economies were rooted in the bursting of leveraged bubbles, while vulnerabilities in emerging markets stemmed from lengthy booms in credit and asset prices combined with growing weaknesses in the corporate and external sectors.

Suggested Citation

  • Mr. Suman S Basu & Mr. Marcos d Chamon & Mr. Christopher W. Crowe, 2017. "A Model to Assess the Probabilities of Growth, Fiscal, and Financial Crises," IMF Working Papers 2017/282, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2017/282
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    Citations

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    Cited by:

    1. Jose González Mínguez & Carmen Martínez Carrascal, 2019. "A crisis early warning model for euro area countries," Economic Bulletin, Banco de España, issue DEC.
    2. Andrea Calef, 2020. "Systemic Banking Crises: The Relationship Between Concentration and Interbank Connections," University of East Anglia School of Economics Working Paper Series 2019-06, School of Economics, University of East Anglia, Norwich, UK..
    3. Mr. Jorge A Chan-Lau, 2020. "UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification," IMF Working Papers 2020/262, International Monetary Fund.
    4. Sondermann, David & Zorell, Nico, 2019. "A macroeconomic vulnerability model for the euro area," Working Paper Series 2306, European Central Bank.
    5. Eduardo C. Castro, 2020. "RegGae: a toolkit for macroprudential policy with DSGEs," Working Papers Series 526, Central Bank of Brazil, Research Department.

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