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An Early Warning Model for Predicting Credit Booms using Macroeconomic Aggregates

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Author Info

  • Alexander Guarín

    ()

  • Andrés González

    ()

  • Daphné Skandalis

    ()

  • Daniela Sánchez

    ()

Abstract

In this paper, we propose an alternative methodology to determine the existence of credit booms, which is a complex and crucial issue for policymakers. In particular, we exploit the Mendoza and Terrones (2008)´s idea that macroeconomic aggregates other than the credit growth rate contain valuable information to predict credit boom episodes. Our econometric method is used to estimate and predict the probability of being in a credit boom. We run empirical exercises on quarterly data for six Latin American countries between 1996 and 2011. In order to capture simultaneously model and parameter uncertainty, we implement the Bayesian model averaging method. As we employ panel data, the estimates may be used to predict booms of countries which are not considered in the estimation. Overall, our findings show that macroeconomic variables contain valuable information to predict credit booms. In fact, with our method the probability of detecting a credit boom is 80%, while the probability of not having false alarms is greater than 92%.

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Bibliographic Info

Paper provided by BANCO DE LA REPÚBLICA in its series BORRADORES DE ECONOMIA with number 009826.

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Length: 17
Date of creation: 22 Jul 2012
Date of revision:
Handle: RePEc:col:000094:009826

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Related research

Keywords: Early Warning Indicator; Credit Booms; Business Cycles; Emerging Markets.;

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  1. Jeffrey A. Frankel & George Saravelos, 2010. "Are Leading Indicators of Financial Crises Useful for Assessing Country Vulnerability? Evidence from the 2008-09 Global Crisis," NBER Working Papers 16047, National Bureau of Economic Research, Inc.
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Cited by:
  1. Carlos Quicazán, . "Profundización Financiera y su efecto en las Firmas en Colombia," Temas de Estabilidad Financiera 070, Banco de la Republica de Colombia.

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