As expected. The November CPI report came in exactly as expected (31bps vs 30bps expected for both, headline and core). Table 1 shows our MoM forecast errors. In the last 6 months, the std error (3bps) and the std dev (8bps) of our MoM forecast for core CPI (our focus) have been very competitive. (in the last 4 months, our headline forecast has been probably the best in the street, as the absolute mean error is 3bps)
Reaction to the incoming data: near-term revised down a touch. We are still digesting the data, but our first reaction is to mark down a bit the near-term, as OER was weaker than expected. We now expect Dec core CPI at 25bps. Consequently, we expect the YoY of core CPI (PCE) at 3.3% (2.9%) in December 2024.
Translation CPI/PCE: our translation of today’s core CPI print is 18bps in core PCE space. Please, be aware that this is tentative and it can change with PPIs.
Evidence from our models: big picture unchanged. In CPI space the distribution of price changes remains different than pre-Covid. Please, note that once again the distribution is suggesting NO progress at all in the last 9 months, contrary to the dominant narrative. The CI model suggests that the common component remains solid and close to 3%. Finally, the “main” medium-term model is unchanged and the forecast remains above the SEP.
As for the Fed: today’s CPI report changes nothing. Please, see our “Pre-FOMC Meeting Package” (here).
Table 1. Updated MoM (sa) UnderlyingInflation real-time forecast errors in the last 6 months.
A PDF containing all relevant CPI charts has been posted. You can download it here.
Evidence from the distributions
Distribution, still unfriendly and not consistent with target. This month, the distribution remains very dispersed, and similar to last month (ridge plot here). The median (Figure 2) ticked down. Looking at Figure 1, the broad picture is unchanged: the distribution remains different than pre-Covid, with limited (or zero) progress in the last 9 month. For this reason, as we wrote in previous notes, we remain careful in declaring victory or claiming that 2% is around the corner.
Figure 1. Kernel of CPI excluding food and energy items changes (MoM %, a.r.)
Note: the Figure shows the fitted Kernel (Epanechnikov) distribution of MoM percent changes at annual rate of CPI prices excluding food and energy items.
Figure 2. Median (core) CPI metrics
Note: the Figure shows the median (MoM %, a.r.) of the distribution of CPI prices changes excluding food and energy items (left panel) and the YoY (right panel).
Evidence from our CI-C model
Our CI model estimates that net of Covid and idiosyncratic shocks, the common component remains above target. Figure 3 shows the decomposition of the MoM of core CPI in the “common” vs “idiosyncratic” component. The model estimates that this month the common component increased by 31bps, while the idiosyncratic shock is null (0bps). The 3m/3m of the “common” component (Figure 4) is at 3.0%. Overall, the evidence of the CI model suggests that the “true” underlying pace of the data remains above target and close to 3%.
Figure 3. Contributions to MoM changes of CPI excluding food and energy items (CI-C model)
Note: the Figure shows the decomposition of the MoM percent changes of CPI prices excluding food and energy items. The contributions are estimated using our CI-C model, a 2-stage OLS-LASSO regression model.
Figure 4. 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 little changed. Today’s data has no impact on our Q4 nowcast (2.7% QoQ saar) in core PCE space, and therefore had no material effect on the model forecast. The (Q4/Q4) model forecast is: 2.9% in 2024, 2.5% in 2025, 2.5% in 2026, and 2.4% in 2027.
Figure 5. “Main” Phillips curve model forecast, core PCE price inflation (YoY, %).
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).