The sharp drop in the fiscal impulse represents a direct, dollar-for-dollar hit to nominal GDP that has already occurred. This indicates a recession is underway, not forthcoming. The National Bureau of Economic Analysis (NBER) will likely backdate the start of this recession to the third quarter of 2025.

The podcast's economists assess the probability of a recession in the next year at 40-45%, significantly higher than the consensus view of 25-30%. This heightened risk is based on deteriorating labor market trends and is corroborated by Moody's own machine learning models.

The economy can likely absorb a temporary spike to $100/barrel oil, supported by fiscal stimulus. However, if prices reach and sustain $120/barrel for a few months, the psychological and financial strain on consumers and businesses would likely trigger a recession.

In a machine learning algorithm designed by Moody's to predict recessions, aggregate building permits (single-family and multifamily) emerged as the single most important variable. A decline in permits is a powerful signal of elevated recession risk for the entire economy.

Despite weak underlying economic data, the probability of a recession is not over 50% due to anticipated policy stimulus. This includes Fed rate cuts, major tax cuts, and deregulation, which are expected to provide significant, albeit temporary, economic support.

The Sahm Rule provides a clear signal that a recession has begun: when the three-month moving average unemployment rate rises by more than 0.5 percentage points above its low from the previous year. This metric is useful for cutting through noise and identifying when a slowly weakening job market has definitively tipped into a downturn.

The team's central economic forecast hinges on the belief that President Trump's sensitivity to falling stock prices and rising gas prices will compel him to de-escalate the conflict with Iran within weeks, preventing a recession.

A Moody's machine learning model, which analyzes leading economic indicators, had already calculated a 48.6% probability of recession *before* the Iran conflict began. The primary driver for this high reading was a deteriorating labor market, indicating underlying economic weakness.

To navigate conflicting economic signals, Moody's built a model that uses a machine learning technique called a random forest. It aggregates 'votes' from numerous decision trees based on economic data, with labor markets carrying the most weight, to produce a single 12-month recession probability.