HICP as US Core PCE
The January HICP report was in line with expectations and suggests relatively moderate readings going forward. The distribution of price changes is centered around target, although a bit more dispersed than in previous months. The CI model suggests that the common component is a bit stronger than in December. Having said that, the two approaches suggest relatively moderate readings (i.e. MoM saar around 2.5%) in the near-term. Finally, the medium-term models are little changed and the risks around the new ECB forecast are well balanced.
Summary: January was a big repricing risk (see the US). In the EA, it seems that the January re-pricing effect was smaller than in other regions. Having said that, unfortunately, the January reading does not solve the puzzle of whether (and when) the EA will return to target. Indeed, the models suggest that both in the near-term and in the medium-term prices can run moderately above the ECB target with upside risks coming from wages (in this sense, the evidence for EA HICP reminds us of US core PCE). In this environment, we expect the ECB to take time at the March meeting.
Evidence from the distributions
The distribution: centered around target. This month, we do not have a clear signal from the distribution as some percentiles moved up and some down (ridge plot here). In the last 3 months (the black line in Figure 1) the distribution remained centered around target with a thicker left tail. Finally, the median (Figure 2) has dropped notably compared to a year ago and it is now a bit above target.
Overall, this evidence continues to suggest near-term readings around target (MoM saar around 2.5%).
Figure 1. Kernel of HICP excluding food and energy items changes (%, a.r.)
Figure 2. Median of HICP excluding food and energy items prices increase
Evidence from our CI model
Our CI model estimates that net of idiosyncratic shocks, the common component across items is stronger than in recent months. Figure 3 shows the decomposition of the MoM of core HICP in the “common” component and the “idiosyncratic” component. The model estimates that in January the common component increased by 25bps, a bit above the average of the previous months, while the idiosyncratic shock is also positive (8bps). As we did in previous months, we consider as “true” core the one netting out the idiosyncratic part. Therefore, a rough estimate put the MoM (saar) of “true” core HICP at around 3% in January, a bit above the previous months. The signal of the CI model in the last few months is in line with the distributions and suggests that core HICP is running around 2.5% at annual rate.
Figure 3. Contributions to MoM changes of HICP excluding food and energy items
Note: the Figure shows the decomposition of the MoM percent changes of HICP prices excluding food and energy items. The contributions are estimated using our CI 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 model.
Implications for the medium-term forecast of core HICP
Medium-term model-based forecast little changed. The models forecasts are slightly higher than the run following the flash release. The reason is that the inclusion of Q1 in-sample has triggered revisions to the SA data in the previous quarters. Unfortunately, these revisions are likely to continue going forward as the filters will need time to understand the post-Covid seasonal pattern. (For the record: we are doing our best and try to correct the SA data. As mentioned, in our simulations, the current quarter is likely to be revised again once Q2 and Q3 will be in sample. We are already trying to account for this effect in our models).
Using the unemployment rate as measure of “slack”, the forecast is at 2.6% (average YoY) in 2024, 2.5% in 2025, and 2.4% in 2026. Using the output gap (right panel in Figure 5), the model delivers a more dovish forecast: 2.5% in 2024, 2.2% in 2025, and 2.0% in 2026. The average of these forecasts is now a bit below the latest ECB/NCBs staff forecasts in 2024 but slightly above it in 2026.
Figure 5. Model-based medium-term forecast of core HICP (YoY)
Using Urate as a measure of “slack”
Using outputp gap as a measure of “slack”
Note: the confidence intervals (C.I.) are calculated using the estimated parameters distributions.
A comparison with the ECB/NCBs staff forecast
Risks around the ECB/NCBs forecasts are balanced. Table 1 shows a comparison between our latest forecast and the ECB/NCBs staff forecast. The 2023 carryover for 2024 is 1.0%-1.1%. This implies that a forecast below 3% for 2024 is reasonable. Going beyond 2024, the models are a touch above the ECB/NCBs staff forecast, as the inflation process is estimated a bit more persistent. The gap between the models and the ECB/NCBs staff forecast is small in 2026, and the main message continues to be “still unsure to reach target”. Risks around this forecast are very well balanced.
(For a technical note on the concepts of “acquired inflation” and “carryover effect” see here and here).
Table 1. Comparison of forecasts
Note: the “UnderlyingInflation” forecast refers to the average of the two models shown in Figure 5.
Implications for the ECB
No big implications for the ECB. The impression is that the forecast was set wisely this time. If anything, our models are a bit more dovish in 2024 but returning to target is still a question mark. For this reason, we expect the ECB to be prudent (“wait and see” mode) at the March meeting.