Early Warning Models for Systemic Banking Crises in Montenegro
The purpose of this research is to create an adequate early warning model for systemic banking crises in Montenegro. The probability of banking crisis occurrence is calculated using discrete dependent variable models, more precisely, estimating logit regression. Afterwards, seven simple logit regressions that individually have two explanatory variables are estimated. Adequate weights have been assigned to all seven regressions using the technique of Bayesian model averaging. The advantage of this technique is that it takes into account the model uncertainty by considering various combinations of models in order to minimize the author’s subjective judgment when determining reliable early warning indicators. The results of Bayesian model averaging largely coincide with the results of a previously estimated dynamic logit model. Indicators of credit expansion, thanks to their performances, have a dominant role in early warning models for systemic banking crises in Montenegro. The results have also shown that the Montenegrin banking system is significantly exposed to trends on the global level.
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