Not at target.
Today’s data confirms that we are still not at target. Below the surface there are some good news (NSA level is more friendly than the SA level, see our charts package) but the distribution of price changes remains different from the pre-Covid period and inconsistent with the target.
The CI model estimates a solid common component. The good news is that in Jan 2024 and Jan 2023 the common component was even stronger than now. 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. Risks remain skewed to the upside, unless a recession hits the economy.
A PPT containing all relevant CPI/PCE charts can be downloaded here.
Evidence from the distributions
Not consistent with target.
This month, the distribution is way more dispersed than last month (Figure 1), as repricing took place in January. 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 or six months. Finally, the median (Figure 3) remains volatile and ticked up this month.
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 30bps, while the idiosyncratic shock was a small negative (-2bps). The 3m/3m annualized rate of the common component (Figure 5) is currently running above target at 2.5%, 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 2.6%).
An Excel file containing the results of the CI model shown in Figure 4 is available here.
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 unrevised. Today’s data had no material impact on our Q1 nowcast (we are still assuming that core PCE prices will growth 2.9% QoQ saar). For this reason, the model forecast is essentially unrevised compared to recent runs. The (Q4/Q4) model forecast is as follows: 2.8% in 2025, 2.6% in 2026, and 2.5% in 2027.
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.