We have updated our Common-Idiosyncratic and Covid (or CI-C) model to include the month of January 2022. Overall, January was a very strong month, influenced by a large positive Covid effect but also a large positive “common” component. The idiosyncratic effect is estimated positive but small in January.
Results
Figure 1 shows the decomposition of the MoM of PCE excluding food and energy items in the “common” component (the blue bars), an “idiosyncratic” component (the yellow bars), and the “Covid” effect (the green bars). Our model estimates that in January the common component expanded 22bps, and that the Covid effect is positive and large (25bps). Given today’s reading, the YoY of the common component is estimated at 2.4 percent in January, one tenth higher than in December (Figure 2 and 3). The 2.4% of the YoY of the common component in January is the highest reading since the beginning of the sample (and the highest over the “anchored” period). Finally, the 3m/3m a.r. and the 6m/6m a.r. of the common component (Figure 4) are estimated at 2.6% and 2.4%, respectively in January suggesting that the YoY will most likely tick up going forward.
Comment
The evidence from the monthly distributions matches well the one from our CI-C model. Unless the level of durable goods will start falling soon (therefore offsetting part of the diffuse strengh of the data), the commmon component should continue trending higher in the next few months.
In our estimates, the Covid effect is currently contributing to the YoY of PCE ex FE by about 1.9 percentage points but we also estimate a large contribution of the common component which, by construction, should prove persistent going forward and which continues to run above the pre-Covid levels (and increasing).
According to the January FOMC minutes, the Fed staff has (finally) revised up its 2022 forecast for core PCE to 2.6 percent. In our view, the Fed staff inflation forecast is finally in line with reality, although we think that the medium-term risks continue to be skewed to the upside.
Figures
Figure 1 Contributions to MoM changes of PCE excluding food and energy items
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 2 Contributions to YoY changes of PCE excluding food and energy items
Note: the Figure shows the decomposition of the YoY 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 3 YoY of PCE prices excluding food and energy items and the “Common” component of the CI-C model
Note: the Figure shows the YoY of PCE excluding food and energy items (black line), and the YoY of the “common component” (blue line) estimated using our CI-C 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.