Abstract:
Based on a large panel dataset of small commercial banks in the United States, this paper employs a dynamic panel Tobit model to analyse the role of uncertainty in forecasting charge-off rates on loans for credit card (CC) and residential real estate (RRE). When compared to other standard predictors, such as house prices and unemployment rates, we find that the effect of uncertainty changes on charge-off rates is more pronounced. Furthermore, it is evident that including heteroscedasticity in the model specification leads to more accurate forecasts.
