At-Risk Transformation for U.S. Recession Prediction

TL;DR

This paper introduces an 'at-risk' binarization method for predictors in U.S. recession forecasting, which improves model performance by capturing the discrete nature of rare economic events.

Contribution

It proposes a simple binarization technique for predictors that enhances recession prediction accuracy over traditional continuous variables.

Findings

Binarized predictors improve out-of-sample forecasting performance.

Linear models become more competitive with machine learning methods.

Forecasting gains are especially notable at recession onsets.

Abstract

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.