At-Risk Transformation for U.S. Recession Prediction

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, 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.