May 31, 2024

US: April 2024 PCE and FRB-US:Q2

FF Rate at 5.2% in Q4

Updated FRB-US baseline. We have updated our FRB-US baseline following the Q1 GDP revisions and including our Q2 nowcasts (2.4% QoQ saar for real GDP growth, in line with the model own forecast for Q2). In Figure 0, the blue solid line shows the March SEP, while the red dashed line shows the FRB-US forecast. The model forecast is marginally weaker, as the weakness in Q1 offset Q2. Nevertheless, the big picture is unchanged: Core PCE remains above target, and the FF rate is projected at 5.2% in 2024:Q4. This updated baseline is likely to remain as such in our pre-FOMC meeting package. The path of the financial variables (i.e. 10y yield) consistent with the updated baseline is here.

Figure 0. Updated FRB-US baseline forecast.

PCE prices report

A PPT containing all relevant CPI/PCE charts can be downloaded here.

Evidence from the distributions

More friendly distribution but higher median. This month, the distribution is a bit more friendly than last month (Figure 1) but in this highly volatile environment we have learned to take no signal from a single month, especially if the NSA level is not sending the same signal. Zooming out, in the last 3 months (Figure 2) there are no signs of progress. Finally, the median (Figure 3) remains volatile and elevated.

To sum up: the distribution continues to suggest near-term readings above target (around 2.5%+ MoM saar).

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

Our CI model estimates a strong common component. 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 in April the common component increased by 21bps, while the idiosyncratic shock is a small positive (4bp). Overall, the common component (Figure 5) seems to have bottomed and it has gone sideways in recent months above target. The signal of the CI model is roughly in line with the one of the distributions.

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 unchanged. The model forecast is little changed, as the Q2 nowcast for core PCE prices is unchanged. The latest model forecast is: 3.2% (Q4/Q4) in 2024, 2.8% in 2025, and 2.7% in 2026. This forecast is above the latest SEP at every horizon (We remind the reader that the current estimate of pi* is 2.6% in core PCE space, unaffected by today’s release)

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).

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