We have updated our Common-Idiosyncratic and Covid (or CI-C) model to include the month of March 2022. Overall, March was a strong month. Our model estimates a strong contribution of the common component, in line with the pace of the last 4-5 months. Net of Covid and idiosyncratic shocks, the strengh of the data remains intact.
Results
Figure 1 shows the decomposition of the MoM of CPI excluding food and energy items in the “common” component (the blue bars), the “idiosyncratic” component (the yellow bars), and the “Covid” effect (the green bars). Our model estimates that in March the common component increased by 22bps, in line with the average of the last 4-5 months. The Covid effect is estimated positive and large (28bps). Finally, the idiosyncratic part is a small negative (-17bps).
Note: in March, used cars prices dropped notably. Therefore, one could expect the model to show a negative Covid effect. However, as mentioned, the model estimates a positive Covid effect (and a negative idiosyncratic effect). This happens because today’s drop in used cars prices is not outside the boundaries of its historical distribution. Therefore, the model does not identify today’s movement as “anomalous” and ends up attributing it to item-specific idiosyncratic effects. In any case, it should be noted that: (i) net of used cars prices, in March several other items had large positive price shocks identifiable as “Covid effect”, (ii) the common component remained very strong, (iii) the idiosyncratic negative shock is estimated smaller than the positive “Covid” effect. This evidence continues to suggest diffuse strenght in the data.
Today’s reading brings the YoY of the common component to 2.7 percent, the same level as the previous month (Figure 2 and 3). At the same time, today’s reading brings the 3m/3m a.r. of the common component at 2.9 percent and the 6m/6m a.r. at 2.7 percent (Figure 4). This evidence suggests that the YoY of the common component will probably tick up again in the upcoming months.
Comment
The results of the CI-C model complement the evidence of the monthly distributions. The model is suggesting that net of Covid and idiosyncratic shocks, in March the common component continued to be strong (as previously communicated, the common component is persistent by construction; therefore, we continue to expect the current strengh of the data to persist in the coming months).
As a matter of comparison, we report in Figure 5 core CPI net of used cars (and trucks) and shelter. This measure of “core” inflation increased 7.3% MoM (a.r.) in March and 7.7% (a.r.) in the last 3 months.
Put it differently, today’s report is a good summary of the debate on inflation we will see for the rest of the year. Some people will comment headline numbers but other will look under the surface. In our view, the evidence continues to be clear: the underlying pace of core inflation is strong (and still trending higher). The Fed can be saved by a series of consecutive contractions of durable goods prices. However, the data ex-cars are so strong right now that used cars prices will need to drop much faster than they did today to moderate core inflation to target areas (worth remembering that core CPI expanded at 4.0% a.r. today despite the drop in used cars). It is possible but it is not the baseline scenario right now.
Figures
Figure 1 Contributions to MoM changes of CPI excluding food and energy items
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. The “Covid” effect is identified with price variations outside the 10th-90th percentiles of each item pre-Covid price change distribution.
Figure 2 Contributions to YoY changes of CPI excluding food and energy items
Note: the Figure shows the decomposition of the YoY 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. The “Covid” effect is identified with price variations outside the 10th-90th percentiles of each item pre-Covid price change distribution.