Not at target. Today’s data reinforce the message that we are not at target. The distribution of price changes continues to be different than pre-Covid and not consistent with target. The CI model estimates a solid common component (and a negative idiosyncratic shock). We also invite the reader to take a look at the NSA level of the series (see below). Finally, the medium-term forecast is marginally weaker, but the big picture is intact. Translated: we have not learned anything new in today’s report
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 more dispersed than last month (Figure 1). Having said that, in this highly volatile environment we have learned to take little signal from a single month or two of data. Indeed, zooming out (Figure 2) the distribution remains different than pre-Covid with little signs of improvements in the last few months. Finally, the median (Figure 3) remains volatile and moved sideways this month. Overall, we are still careful taking signal because of possible residual seasonality and because, as mentioned, the distributions are still not consistent with target.
A note: while the MoM sa have become more volatile than pre-Covid, the NSA level of the series is more clear (see here) and right now is sending similar signal than the distributions and the CI model.
Figure 1. Distribution of PCE excluding food and energy items changes (%, a.r.)
Note: The Figure shows the fitted Kernel (Epanechnikov) distribution of MoM percent changes at annual rate of PCE prices excluding food and energy items. The colors indicate the percentiles: 0-10pct, 10-25pct, etc. The dashed line shows the median of the distribution.
Figure 2. Kernel of PCE excluding food and energy items changes (%, a.r.)
Note: The Figure shows the fitted Kernel (Epanechnikov) distribution of MoM percent changes at annual rate of PCE prices excluding food and energy items.
Figure 3. Median PCE price increase
Note: The Figure shows the median (MoM %, a.r.) of the distribution of PCE prices changes excluding food and energy items (left panel) and the YoY (right panel).
Evidence from our Common-Idiosyncratic (CI) model
Common component above target. Figure 4 shows the decomposition of the MoM of core PCE in the “common” component (blue bars) and the “idiosyncratic” component (yellow bars). The model estimates that this month the common component increased by 17bps, while the idiosyncratic shock is negative (-5bp). The 3m/3m ar of the common component (Figure 5) is now running above target (at 2.6%) and we expect it to remain around this level in the next few months. Overall, the signal of the common component (Figure 5) is roughly in line with the one 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 shows the decomposition of the MoM percent changes of PCE prices excluding food and energy items. The contributions are estimated using our CI-C model, a 2-stage OLS-LASSO regression model. The “Covid” effect is identified with price variations outside the 10th-90th percentiles of each item pre-Covid price change distribution.
Figure 5. Estimated “Common” component: YoY, 3m/3m a.r. and 6m/6m a.r.
Note: the Figure shows the 3m/3m at annual rate (green line), the 6m/6m at annual rate (red line), and the YoY (blue line) of the “common component” estimated using our CI-C model.
Implications for the medium-term forecast of core PCE price inflation
The medium-term forecast is slightly lower. While today’s surprise is small, it is enough to revise down our QoQ forecast for Q4. We now work under the assumption that core PCE price inflation will grow 2.5% QoQ saar in Q4. The revised (Q4/Q4) model forecast is: 2.8% in 2024, 2.45% in 2025, 2.4% in 2026, and 2.4% in 2027. The new forecast is only cosmetically different compared to the one at the time of the December FOMC meeting.
Note: the information set of the model does not include any possible tariffs. We will include them if and when they will be passed.
Note: The figure shows the latest run of our “main” Phillips curve model. The confidence intervals (C.I.) are estimated using quasi-out-of-sample methods (estimate the model over a sub-sample, forecast, and calculate the root mean squared forecast errors).