A recent econometric analysis covering the volatile period from late 2020 to late 2025 reveals a striking paradox in how we measure inflation. Utilizing WarpPLS () to conduct a nonlinear robust path analysis, we found that the association between Brent Crude oil prices and the Core Personal Consumption Expenditures (PCE) price index stands at a remarkable 0.78. Perhaps most significantly, the WarpPLS Nonlinear Bivariate Causality Direction Ratio (NLBCDR) suggests a causal link, indicating that fluctuations in oil prices are likely a primary driver of the Core PCE’s movement. This challenges the conventional wisdom of inflation tracking, as it suggests that oil prices account for approximately 60 percent of the variance in a metric specifically designed to exclude energy costs.
The nature of this relationship is distinctly nonlinear, characterized by a strong correlation that eventually hits a ceiling. Within the price range of approximately $54 to $105 per barrel, the linear association (correlation) between Brent Crude and Core PCE is a high 0.91. In this range, a $1 increase in the price of oil is associated with a 0.08 increase in Core PCE inflation. However, once oil prices exceed the $105 threshold, the relationship turns flat, suggesting a diminishing marginal impact on core inflationary pressures at very high oil price levels. This "warped" relationship explains why core inflation can feel so tethered to the gas pump during moderate price climbs, yet appears decoupled during historically high oil spikes.
These findings necessitate a fundamental re-thinking of the Core PCE measure as a tool for monetary policy. If "Core" inflation is intended to strip out volatile elements to reveal the underlying price trend, its 60 percent dependency on oil—one of the very elements it seeks to exclude—suggests that energy costs are far more "sticky" and systemic than previously assumed. For investors and policymakers, this means that Core PCE may not be a very reliable measure of domestic demand, but rather a lagging reflection of energy-driven supply chain costs. Understanding this nonlinear dependency is crucial for anticipating Fed shifts in an era of energy transition.
