This study proposes using the Least Absolute Shrinkage and Selection Operator (LASSO) method with cross-validation to automate the variable selection process of the conventional multivariate logit early warning system (EWS), the purpose being to improve the prediction of systemic banking crises. Using a dataset covering 23 OECD countries with quarterly data from 1970Q1 to 2018Q3, model performance is evaluated in a recursive out-of-sample forecasting exercise, taking policy-makers' preference of missed crises and false alarms into account. The results suggest that the automatic variable selection process can enhance the predictive performance of the EWS. It also highlights the importance of extracting information from variable interactions and lags that may not be easily identified and accessed by typical subjective variable pre-selection. This simple approach is easy to interpret and is transparent, which are important aspects for effective policy communication. Five variables, namely credit growth, domestic and global credit gaps, real house price growth and the real effective exchange rate, are identified as the most important key indicators of systemic banking crises.

中文翻译：

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实现更好的银行危机预测：自动变量选择过程能否提高绩效？*

本研究提出使用具有交叉验证的最小绝对收缩和选择算子 (LASSO) 方法来自动化传统多变量 logit 预警系统 (EWS) 的变量选择过程，目的是提高对系统性银行危机的预测。使用涵盖 23 个 OECD 国家的数据集以及从 1970Q1 到 2018Q3 的季度数据，在递归样本外评估模型性能预测工作，考虑决策者对错过危机和误报的偏好。结果表明，自动变量选择过程可以提高 EWS 的预测性能。它还强调了从变量交互和滞后中提取信息的重要性，这些信息可能不容易通过典型的主观变量预选来识别和访问。这种简单的方法易于解释且透明，这是有效政策沟通的重要方面。五个变量，即信贷增长、国内和全球信贷缺口、实际房价增长和实际有效汇率，被确定为系统性银行危机最重要的关键指标。