An Early Warning Model for Predicting Credit Booms Using Macroeconomic Aggregates

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's (2008) idea that macroeconomic aggregates contain valuable information to predict lending boom episodes. Specifically, 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 relevant information to identify and 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%.

[1]  C. Cottarelli,et al.  Early Birds, Late Risers, and Sleeping Beauties : Bank Credit Growth to the Private Sector in Central and Eastern Europe and in the Balkans , 2003 .

[2]  An Anatomy of Credit Booms: Evidence from Macro Aggregates and Micro Data , 2008 .

[3]  R. Hausmann,et al.  The Roots of Banking Crises: The Macroeconomic Context , 1996 .

[4]  Oscar Landerretche,et al.  Lending Booms: Latin America and the World , 2001 .

[5]  L. Rojas-Suárez,et al.  Credit at Times of Stress: Latin American Lessons from the Global Financial Crisis , 2012 .

[6]  C. Borio,et al.  Asset Prices, Financial and Monetary Stability: Exploring the Nexus , 2002 .

[7]  D. Furceri,et al.  Episodes of Large Capital Inflows and the Likelihood of Banking and Currency Crises and Sudden Stops , 2011 .

[8]  J. Frankel,et al.  Are Leading Indicators of Financial Crises Useful for Assessing Country Vulnerability? Evidence from the 2008-09 Global Crisis , 2010 .

[9]  P. Montiel.  Capital Flows: Issues and Policies , 2013, Open Economies Review.

[10]  A. Raftery.  Bayesian Model Selection in Social Research , 1995 .

[11]  Evan Kraft,et al.  Does speed kill? Lending booms and their consequences in Croatia , 2005 .

[12]  S. Brooks,et al.  Classical model selection via simulated annealing , 2003, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

[13]  J. Ostry.  Managing Capital Flows: What Tools to Use? Asian Development Review, Vol. 29(1), pp. 82-88 , 2012 .

[14]  G. Kiss,et al.  Credit Growth in Central and Eastern Europe: Convergence or Boom? , 2006 .

[15]  A. Levchenko,et al.  Credit Booms : the Good , the Bad , and the Ugly , 2007 .

[16]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[17]  An Anatomy of Credit Booms: Evidence from Macro Aggregates and Micro Data , 2008 .

[18]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[19]  Ensayos sobre Política Económica , 2022 .

[20]  Andrés Velasco,et al.  Financial Crises in Emerging Markets: The Lessons from 1995 , 1996 .

[21]  L. Laeven,et al.  Credit Booms and Lending Standards : Evidence From the Subprime Mortgage Market , 2008 .

[22]  P. Green,et al.  Reversible jump MCMC , 2009 .

[23]  J. Walsh,et al.  The Global Financial Crisis: Explaining Cross-Country Differences in the Output Impact , 2012, SSRN Electronic Journal.

[24]  Carmen M. Reinhart,et al.  The Twin Crises: The Causes of Banking and Balance-of-Payments Problems , 1996 .

[25]  D. Madigan,et al.  Bayesian Model Averaging for Linear Regression Models , 1997 .