A1 Refereed original research article in a scientific journal

Predicting banking crises with artificial neural networks: The role of nonlinearity and heterogeneity




AuthorsKim Ristolainen

PublisherWiley-Blackwell

Publication year2018

JournalScandinavian Journal of Economics

Volume120

Issue1

First page 31

Last page62

Number of pages32

ISSN0347-0520

DOIhttps://doi.org/10.1111/sjoe.12216(external)

Web address http://onlinelibrary.wiley.com/doi/10.1111/sjoe.12216/abstract;jsessionid=84E991400EDD7FF223D439DF392F1193.f02t03(external)


Abstract

The early warning system (EWS) literature on banking crises usually relies on linear
classifiers, estimated with international datasets. We construct an EWS based on an artificial
neural network (ANN) model, and also account for regional heterogeneity in order to improve
the generalization ability of EWS models. All of the banking crises in our test set are then
predictable at a 24 month horizon, using information from earlier crises. For some countries,
estimation with a regional dataset significantly improves the predictions. The ANN
outperforms the usual logit regression, assessed by the area under the receiver operating
characteristics curve.



Last updated on 2024-26-11 at 15:31