The purpose of this study is to Prediction the possibility of a banking crisis in a dynamic early warning system framework. We using data compiled from 10 middle-income countries for the period 1996-2017 and estimate static and dynamic logit model. The estimation results show that the dynamic logit model is better than the static model. We find that broad liquidity ratio, domestic credit to GDP ratio and stock market index are early warning signs of banking crisis. Also, the dynamic variable (lagged banking crisis) shows that if a banking crisis occurs in a year ago, there is a possibility of crisis in this year. Our result shows the possibility of continuation and persistence of banking crises for consecutive years. Then, the evaluation results of warning system show dynamic early warning system turns out to exhibit significantly better predictive abilities than the existing static one, both in- and out-of-sample.
akbar mousavi, S. S., Salmani, B., Haghighat, J., & Asgharpour, H. (2022). Banking Crisis Prediction: A Dynamic Early Warning System. Journal of Econometric Modelling, 7(1), 9-38. doi: 10.22075/jem.2022.25793.1686
MLA
seyed saleh akbar mousavi; Behzad Salmani; Jafar Haghighat; Hossein Asgharpour. "Banking Crisis Prediction: A Dynamic Early Warning System", Journal of Econometric Modelling, 7, 1, 2022, 9-38. doi: 10.22075/jem.2022.25793.1686
HARVARD
akbar mousavi, S. S., Salmani, B., Haghighat, J., Asgharpour, H. (2022). 'Banking Crisis Prediction: A Dynamic Early Warning System', Journal of Econometric Modelling, 7(1), pp. 9-38. doi: 10.22075/jem.2022.25793.1686
VANCOUVER
akbar mousavi, S. S., Salmani, B., Haghighat, J., Asgharpour, H. Banking Crisis Prediction: A Dynamic Early Warning System. Journal of Econometric Modelling, 2022; 7(1): 9-38. doi: 10.22075/jem.2022.25793.1686
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