Predicting U.S. Bank Failures with MIDAS Logit Models
提出一种基于MIDAS Logit模型的银行倒闭预测方法，通过处理数据不平衡和引入混合频率采样，在2004-2016年美国银行数据上比传统Logit模型更准确地预测了倒闭案例，尤其是长期预测。
We propose a new approach based on a generalization of the logit model to improve prediction accuracy in U.S. bank failures. Mixed-data sampling (MIDAS) is introduced in the context of a logistic regression. We also mitigate the class-imbalance problem in data and adjust the classification accuracy evaluation. In applying the suggested model to the period from 2004 to 2016, we show that it correctly classifies significantly more bank failure cases than the classic logit model, in particular for long-term forecasting horizons. Some of the largest recent bank failures in the United States that had been previously misclassified are now correctly predicted.