Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity

Kim Ristolainen

Scandinavian Journal of Economics, 2018, vol. 120, issue 1, 31-62

Abstract: Studies of the early warning systems (EWSs) for banking crises usually rely on linear classifiers, estimated with international datasets. I construct an EWS based on an artificial neural network (ANN) model, and I also account for regional heterogeneity in order to improve the generalization ability of EWS models. All of the banking crises in my 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.

Date: 2018
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