Not at target.
Today’s data confirms that we are still not at target. The December report is a step in the right direction, but the distribution of price changes remains different from the pre-Covid period and inconsistent with the target.
The CI model estimates a weak common component, but more evidence is needed—this pattern also appeared at the end of last year. For this reason, we remain cautious.
Finally, the medium-term forecast remains unchanged and stays above target. In short, today’s report hasn’t told us much, and we need more evidence.
A PPT containing all relevant CPI/PCE charts can be downloaded here.
Evidence from the distributions
Not consistent with target.
This month, the distribution looks very similar to last month (Figure 1). Taking a broader view (Figure 2), the distribution remains different from the pre-Covid period, with little sign of improvement in recent months—in fact, over the last three months, it has been nearly identical to the previous three months.
Finally, the median (Figure 3) remains volatile and ticked down this month. However, note that the YoY median is trending higher.
Overall, we remain cautious in interpreting the signals due to potential residual seasonality and, as mentioned, because the distributions are still not consistent with the target.
Figure 1. Distribution of PCE excluding food and energy items changes (%, a.r.)
Note: The figure displays the fitted Kernel (Epanechnikov) distribution of the MoM percent changes at an annualized rate for PCE prices excluding food and energy items. The colors represent different percentiles (0-10th, 10-25th, etc.), while the dashed line indicates the median of the distribution.
Figure 2. Kernel of PCE excluding food and energy items changes (%, a.r.)
Note: The figure presents the fitted Kernel (Epanechnikov) distribution of the MoM percent changes at an annualized rate for PCE prices excluding food and energy items.
Figure 3. Median PCE price increase
Note: The figure displays the median MoM % (annualized rate) of the distribution of PCE price changes excluding food and energy items (left panel) and the YoY rate (right panel).
Evidence from our Common-Idiosyncratic (CI) model
Common component above target.
Figure 4 illustrates the decomposition of the MoM core PCE into the “common” component (blue bars) and the “idiosyncratic” component (yellow bars). This month, the model estimates that the common component increased by 14bps, while the idiosyncratic shock remains small (2bps).
The model suggests that the common component was on the weaker side in December, but we remain cautious, as similar patterns were observed in May and toward the end of last year. Indeed, the 3m/3m annualized rate of the common component (Figure 5) is currently running above target at 2.6%, and we expect it to stay around this level in the coming months.
Overall, the signal from the common component (Figure 5) is broadly aligned with that of the distributions and the latest estimate of pi* (at 2.4%).
Figure 4. Contributions to MoM changes of PCE excluding food and energy items (CI-C model)
Note: The figure presents the decomposition of the MoM percent changes in PCE prices excluding food and energy. Contributions are estimated using our CI model.
Figure 5. Estimated “Common” component: YoY, 3m/3m a.r. and 6m/6m a.r.
Note: The figure displays the 3m/3m annualized rate (green line), the 6m/6m annualized rate (red line), and the YoY rate (blue line) of the “common component,” as estimated using our CI model.
Implications for the medium-term forecast of core PCE price inflation
The medium-term forecast unchanged. Today’s release did not impact the Q4 figure and had no material effect on the forecast. The revised Q4/Q4 model projections remain: 2.4% in 2025, 2.3% in 2026, and 2.3% in 2027. The new forecast is virtually unchanged from the one presented at the time of the January FOMC meeting.
The model’s information set does not account for any potential tariffs. These will be incorporated if and when they are implemented.
Note: The figure presents the latest run of our “main” Phillips curve model. The confidence intervals (C.I.) are estimated using quasi-out-of-sample methods—specifically, by estimating the model over a sub-sample, generating forecasts, and calculating the root mean squared forecast errors.