Rahul Billakanti, Minchul Shin
arXiv 8 Mar 2026 · Econometrics
arXiv:2603.07813 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | McCracken, Michael W and Ng, Serena (2016) FRED-MD: A monthly database for macroeconomic research | 0.894 | 7 | 4 | 71% |
| 2 | Vrontos, Spyridon D and Galakis, John and Vrontos, Ioannis D (2021) Modeling and predicting US recessions using machine learning techniques | 0.843 | 3 | 3 | 100% |
| 3 | Qi, Min (2001) Predicting US recessions with leading indicators via neural network models | 0.737 | 3 | 3 | 67% |
| 4 | Estrella, Arturo and Mishkin, Frederic S (1996) The yield curve as a predictor of US recessions | 0.737 | 3 | 3 | 67% |
| 5 | Keilis-Borok, V. and Stock, J. H. and Soloviev, A. and Mikhalev, P (2000) Pre-recession Pattern of Six Economic Indicators in the USA | 0.644 | 2 | 2 | 100% |
| 6 | Ng, Serena (2014) Boosting recessions | 0.644 | 2 | 2 | 100% |
| 7 | Döpke, Jörg and Fritsche, Ulrich and Pierdzioch, Christian (2017) Predicting recessions with boosted regression trees | 0.511 | 2 | 2 | 50% |
| 8 | Geoffrey H. Moore (1961) Diffusion Indexes, Rates of Change, and Forecasting | 0.511 | 2 | 1 | 100% |
| 9 | Chen, A. and Roll, R. and Rossiter, R (2011) Forecasting the probability of US recessions: A Probit and dynamic factor modelling approach | 0.405 | 1 | 1 | 100% |
| 10 | Daniele, Maurizio and Kronenberg, Philipp and Reinicke, Tim (2025) Targeted Transformations for Macroeconomic Forecasting | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 57 scored citations.