At-Risk Transformation for U.S. Recession Prediction
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910045992648704 |
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| author | Billakanti, Rahul Shin, Minchul |
| author_facet | Billakanti, Rahul Shin, Minchul |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_07813 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | At-Risk Transformation for U.S. Recession Prediction Billakanti, Rahul Shin, Minchul Econometrics Applications 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. |
| title | At-Risk Transformation for U.S. Recession Prediction |
| topic | Econometrics Applications |
| url | https://arxiv.org/abs/2603.07813 |