An Early Warning Model for Predicting Credit Booms Using Macroeconomic Aggregates


Series: 
Working Papers
Number: 
723
July
2012
Author(s) / Editor(s): 
Alexander Guarín
Andrés González
Daphné Skandalis
Daniela Sánchez
Publishing House: 
Banco de la República
JEL Classification: 

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%.

The opinions expressed here are those of the authors and do not necessarily represent neither those of the Banco de la República nor of its Board of Directors. As usual, all errors and omissions in this work are our responsibility. 

Category / Classification: 
Documentos en elaboración

Contenido disponible en / Available in:

  • Español
  • English

This content has been translated into English for informational purposes. Upon any query regarding its interpretation or enforceability, the Spanish version shall be deemed official, and will prevail over the English version. The authors of specific texts such as working papers or articles select the language of publication; therefore, there might be cases in which the content may only be available in English. 

-