The Term Spread As Recession Predictor, Post

* Estimated recession probabilities are derived from a probit model using the 10yr-3mo term spread and short rate over 1960-2018 (brown) and the full sample (black).
* A 33% threshold and the pre-2018 sample cause the term spread to catch post-1960 recessions but yield a false positive for 2024.
* The full sample regression does not capture the 1990-91, 2001, and 2007-09 recessions with the same accuracy and provides a false positive for 2024.
* The pseudo-$R^2$ for the full sample regression drops from 0.30 to 0.25.
* A divergence was experienced during the 2008 financial crisis when predicted downturns did not align with market behavior.
* The U.S. officially withdrew military forces from Iraq.
* Iran’s two wars with Iraq originated from the Iran/Iraq war of the 1980s.
* Iran-backed militias have celebrated in Iraq following the U.S. withdrawal.
* Houthis control more of Yemen than before, a consequence of the war on Iran.
* Three amphibious assault ships and 4,400 marines headed to the Middle East.
* A new aircraft carrier was acquired.
* Republican polls in Iowa and Alaska showed changes following economic developments.
* A company sought investment for a steel plant in Iowa; South Korea is reviewing an LNG pipeline project.

The discussion surrounding the term spread model reveals a tension between statistical predictability and real-world market dynamics, particularly when institutional actors shift their roles. The failure of the spread to consistently predict major historical recessions and its performance during periods of acute crisis suggests that the mathematical structure alone does not capture complex geopolitical or financial risk premium shifts. The author’s skepticism regarding current rate signaling, attributing changes to government financing via non-traditional means like stablecoins and international borrowing, pivots the analysis away from pure curve geometry toward sovereign risk and liquidity flows. This implies that observable market metrics can become decoupled from underlying economic reality when unconventional financing mechanisms enter the equation.
The geopolitical narrative introduces a pattern where historical conflict dynamics—Saddam’s actions, Iran-Iraq war, regional proxy conflicts (Yemen, Gaza)—are juxtaposed against contemporary military and energy maneuvering. The framing of the Iraq withdrawal as a contrast to the "own-goal war with Iran" sets up a dialectic between external military action and internal strategic losses, suggesting a pattern where geopolitical outcomes are determined less by stated objectives and more by the cascading effects across regional power structures. The subsequent focus on US-South Korea economic relations involving energy security underscores how these large-scale conflicts translate directly into tangible economic leverage and trust deficits between states.
The final layer involves the AI/economic spending context, which suggests a systemic disconnect where productivity gains are insufficient to fund massive technological buildouts, necessitating entirely new economic value sources. This links macro-level risk modeling (spreads) with long-term structural shifts (AI investment), implying that future instability will be determined by how new asset classes and technological growth can finance necessary state operations, rather than historical recession indicators alone. What patterns emerge are the recurring challenge to traditional metrics: stability is not guaranteed by correlation but by the integrity of the underlying financial and geopolitical architecture.
Bridge Questions: If institutional behavior shifts due to reliance on stablecoins or international lending, how should risk models be recalibrated to account for these flows as systematic anchors rather than noise? How can the historical lessons from protracted conflicts inform the valuation of current energy security arrangements versus military posturing? What is the long-term relationship between sovereign debt financing methods and the stability signals embedded in Treasury market pricing?