Early and Accurate Recession Detection Using Classifiers on the Anticipation-Precision Frontier

This paper develops a new algorithm for detecting US recessions in real time. The algorithm constructs millions of recession classifiers by combining unemployment and vacancy data to reduce detection noise. Classifiers are then selected to avoid both false negatives (missed recessions) and false positives (nonexistent recessions). The selected classifiers are therefore perfect, in that they identify all 15 historical recessions in the 1929--2021 training period without any false positives. By further selecting classifiers that lie on the high-precision segment of the anticipation-precision frontier, the algorithm optimizes early detection without sacrificing precision. On average, over 1929--2021, the classifier ensemble signals recessions 2.2 months after their true onset, with a standard deviation of detection errors of 1.9 months. Applied to May 2025 data, the classifier ensemble gives a 71% probability that the US economy is currently in recession. A placebo test and backtests confirm the algorithm's reliability. The classifier ensembles trained on 1929--2004, 1929--1984, and 1929--1964 data in backtests give a current recession probability of 58%, 83%, and 25%, respectively.