At-Risk Transformation for U.S. Recession Prediction

Rahul Billakanti () and Minchul Shin

No 25-34, Working Papers from Federal Reserve Bank of Philadelphia

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, 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

Keywords: Recession Forecasting; Machine Learning; Feature Engineering; At-Risk Transformation; Binarized Predictors; Diffusion Index (search for similar items in EconPapers)
JEL-codes: C25 C53 E32 E37 (search for similar items in EconPapers)
Pages: 47
Date: 2025-10-30
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets and nep-for
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DOI: 10.21799/frbp.wp.2025.34

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