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When to Lean against the Wind
Author(s) -
RICHTER BJÖRN,
SCHULARICK MORITZ,
WACHTEL PAUL
Publication year - 2021
Publication title -
journal of money, credit and banking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.763
H-Index - 108
eISSN - 1538-4616
pISSN - 0022-2879
DOI - 10.1111/jmcb.12701
Subject(s) - boom , sorting , loan , sonic boom , economics , monetary economics , house price , econometrics , finance , computer science , engineering , geology , oceanography , aerospace engineering , supersonic speed , programming language
In this paper, we show that policymakers can distinguish between good and bad credit booms with high accuracy and they can do so in real time. Evidence from 17 countries over nearly 150 years of modern financial history shows that credit booms that are accompanied by house price booms and a rising loan‐to‐deposit ratio are much more likely to end in a systemic banking crisis than other credit booms. We evaluate the predictive accuracy for different classification models and show that characteristics observed in real time contain valuable information for sorting the data into good and bad booms.