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ECONOMY: U.S 1Q2026 Report

By RecessionALERT on May 1, 2026 in Reflections

The Q1 2026 GDP advance estimate prints at 2.0% on three non-repeatable tailwinds — a federal payroll rebound, a tariff-driven inventory surge, and an AI capex pulse accounting for up to 79% of the print on a gross basis. Strip those out and the underlying pace is closer to the high-1s. Core PCE jumped 160 basis points in a single quarter to 4.3%. The Fed cannot cut into 4% inflation and cannot hike into a slowing consumer. The policy trap is set.

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Recession Probability Roundup : Elevated levels

By RecessionALERT on April 26, 2016 in Reflections

NOTE : This is a subscriber-only article that was made open for public viewership on 20 May 2016. A few subscribers have been concerned by the recent jump in recession odds of the Headwinds Index model to 60% A probability of recession of 60% most certainly also implies probability of no recession of 40% and of course this is related directly to any false positives that may (and have) occurred in the past (see March 2003 in above chart.) You […]

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IMPORTANT DISCLAIMERS: Although many of our models rely heavily on back-testing, optimisation and probability methods, please note that past performance is NO GUARANTEE for future returns. No system devised by man can predict the future, let alone the future of the markets and economies. What we have are sets of mathematical models that use historical data and varying theories to pinpoint places in time when the STATISTICAL LIKELYHOOD of calling the start and end to recessions is the greatest. In the midst of all the best mathematical models, you can have geo-political events, wars, terrorist attacks, natural disasters and even nuclear accidents that tip everything upside down. This is always a risk factor you need to factor in on any recession call no matter how confident you are in a signal. The SuperIndexes are created through mathematical and statistical optimisation techniques that best fit the historical NBER data. There is no guarantee that out-of-sample performance will match that of prior in-sample performance.