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Recession Detection Using Real Time GDP Data

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  • Neha Sikand
  • Rongjin Zhang

Abstract

This paper examines whether real-time GDP announcements can reliably identify business-cycle turning points. Using U.S. real-time GDP vintages from 1947 to 2021, we construct 4,356 recession indicators based on alternative smoothing methods and scaling variations. We then combine these indicators with alternative thresholds to generate 137,457 perfect recession classifiers. The selected classifiers identify all 12 historical recessions without generating false positives or false negatives. Restricting attention to the high-precision segment yields two classifiers with a standard deviation of detection errors below three months, while the selected ensemble signals recessions, on average, 3.04 months after their official onset. The framework accurately identifies recession episodes across vintages, suggesting that discrepancies in prior work may reflect limitations of traditional dating methods in addition to data revisions. Overall, the results indicate that real-time GDP announcements provide a practical proxy for NBER-style recession dating.

Suggested Citation

  • Neha Sikand & Rongjin Zhang, 2026. "Recession Detection Using Real Time GDP Data," Papers 2606.00989, arXiv.org.
    • Handle: RePEc:arx:papers:2606.00989
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    References listed on IDEAS

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    1. Michaillat, Pascal, 2025. "Recession Detection Using Classifiers on the Anticipation-Precision Frontier," CEPR Discussion Papers 20933, Centre for Economic Policy Research.
    2. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
    3. Pascal Michaillat, 2025. "Early and Accurate Recession Detection Using Classifiers on the Anticipation-Precision Frontier," NBER Working Papers 34015, National Bureau of Economic Research, Inc.
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