A Simple Real-Time Algorithm to Identify Turning Points in U.S. Business Cycles

Abstract

Abstract We describe a simple online (i.e. real-time) algorithm that identifies both recessions and expansions in the U.S. remarkably well using only the U-3 unemployment rate and the slope of the yield curve, and with only two false alarms from 1960–2024. More often than not, the transitions it identifies are no more than a month removed from the officially determined dates that are published after a long and variable delay by the NBER's Business Cycle Dating Committee. Its data requirements are minimal, and can be obtained from the Federal Reserve's FRED database within the first week of each month. For reasons I do not understand, it appears that the overall unemployment rate acts a “pulse” of the economy that behaves as a near-complete information set for its state. It is my belief that more accurate real-time forecasts will be obtained not by sophisticated mathematical modeling, but by identifying and combining multiple pulses of the economy that beat in near-synchrony with business cycles. As of early September 2024, the algorithm suggests that the U.S. economy entered a recession in July 2024 and that the recession continued in August 2024. In spite of its good performance at detecting business cycles, tests show that it has minimal implications for market timing.

Keywords: Recession, Expansion, Business Cycle, Sahm Rule, Joshi Rule