We propose a simple binarization of predictors, an "at-risk" transformation, as an alternative to the standard practice of using continuous, standardized variables in recession forecasting models. By converting predictors into indicators of unusually weak states based on a thresholding rule estimated from training data, we demonstrate their ability to capture the discrete nature of rare events such as U.S. recessions. Using a large panel of monthly U.S. macroeconomic and financial data, we show that binarized predictors consistently improve out-of-sample forecasting performance, often making linear models competitive with flexible machine learning methods, and that the gains are particularly pronounced around the onset of recessions.

翻译：我们提出一种简单的预测变量二值化方法，即"风险转换"，作为经济衰退预测模型中连续标准化变量标准实践的替代方案。通过将预测变量转换为基于训练数据估计阈值规则所定义的异常疲弱状态指标，我们证明了该方法能够捕捉美国经济衰退等罕见事件的离散特性。利用美国宏观经济和金融月度数据的大规模面板，我们证明二值化预测变量能持续提升样本外预测性能，通常使线性模型能够与灵活的机器学习方法相媲美，且这种改进在经济衰退起始阶段尤为显著。