Recession Detection Using Classifiers on the Anticipation-Precision Frontier

TL;DR

This paper presents a real-time recession detection algorithm that constructs and selects classifiers based on unemployment and vacancy data, achieving early and accurate detection of US recessions with minimal false positives.

Contribution

It introduces a novel classifier ensemble approach that combines anticipation and precision to detect recessions early and accurately, outperforming traditional methods in speed and reliability.

Findings

Perfectly identified all historical recessions in training data

Signals recessions on average 2.1 months after onset

Detects recessions faster than the NBER committee

Abstract

This paper develops an algorithm for detecting US recessions in real time. The algorithm constructs hundreds of millions of recession classifiers by combining unemployment and vacancy data. Classifiers are then selected to avoid both false negatives (missed recessions) and false positives (nonexistent recessions). The selected classifiers are perfect in a statistical sense: 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 delivers early detection without sacrificing accuracy. On average between 1929 and 2021, the selected classifier ensemble signals recessions 2.1 months after their true onset, with a standard deviation of detection errors of 1.8 months. The classifier ensemble is much faster than the NBER Business Cycle Dating Committee: between 1979 and 2021, the committee takes on average 6.3 months to determine recession starts, while the classifier ensemble only takes 1.2 months. Applied to September 2025 data, the classifier ensemble gives a 64% probability that the US economy has entered a recession. A placebo test and backtests confirm the algorithm's reliability.