We have updated our “main” model (as a reminder, the “main” model is an augmented Phillips curve model with anchored expectations as described in Detmeister et al. (2014) and assumes a flat pi* both in history and in the forecast). As usual, our forecast includes a point forecast, as well as two types of confidence bands: A. confidence bands calculated using 100,000 draws from estimated parameters’ distributions, and B. confidence bands on the model’s historical forecast errors (quasi out-of-sample exercise).
This is the first time we include 2022:Q1 in the sample (our nowcast for the current quarter is +5.5% QoQ at annual rate). The forecast extends to 2024:Q4. Compared to the last update, the starting point of the forecast is higher due to the incoming data and we have a slightly tighter labor market.
Results
The inclusion of 2022:Q1 in the sample has triggered an additional upward revision to the already elevated forecast of core PCE price inflation. The model is currently forecasting elevated inflation not only in 2022 but throughout the entire forecast horizon (with upward risks around the point forecast). Notably, the model is now forecasting core PCE price inflation to remain above 3 percent at the end of the medium-term (2024).
Comparison with previous forecasts
Compared to the time of the January 2022 FOMC meeting, the model forecast is stronger in each year of the forecast horizon. Specifically, the model is now expecting core PCE price inflation Q4/Q4 to be 3.6% in 2022 (compared to 3.2% at the time of last FOMC), 3.3% in 2023 (2.8% at the time of last FOMC) and 3.0% in 2024 (2.5% at the time of last FOMC). As we previously discussed, without a sequence of weak incoming data, at this point the “main model” will continue to deliver a forecast significantly above the Fed target. Nevertheless, the evidence is becoming a bit alarming considering that the “main model” is an anchored model (with long-term expectations set at target). Put it differently, the model is mean-reverting by construction. But even conditional on this mean-reversion property, the forecast of the model remains well above 2% at the end of the medium-term because the starting point of the forecast is now too high.
A word of caution about the model forecast
Our “main” model is a workhorse augmented Phillips curve model, extremely popular in central banks, including at the Fed. Nevertheless, at any given point in time the information set of the econometrician is larger than the model. In this moment the information set of the econometrician is based on the narrative of the incoming data, especially about the level of durable goods. Put it differently, there is a reason to believe that the model might be overestimating future inflation if one thinks that the level of (durable) goods will drop significantly in the upcoming quarters.
Implications for the Fed Board staff
We employ our “main” model as a way to assess the risks around the Fed staff forecast. Under normal circumstances, in our view there is only a small difference between the model’s forecast and the staff medium-term “decomposition”. The Fed Board staff forecast (as inferred from the FOMC minutes) is significantly lower in 2022 (and beyond) than the “main” model despite the fundamental assumptions -such as the level of underlying inflation- are the same in our view. As previously discussed, the staff is working under the assumption that its own information set is larger than the model. Under the staff view, most of the recent strength will prove transitory, as the level of durable goods will reverse in the upcoming quarters and services inflation will moderate. The staff might prove to be right; however, in this moment we think the staff forecast does not balance the risks. Indeed, all our models (Phillips curve – thick modelling – trend models) suggest that the risks around the staff forecast continue to lie to the upside.
Figures
Inflation forecast (YoY of core PCE, %) from "main" model – latest run (2022:Q1 in sample)
A. C.I. calculated from parameters distributions
Note: the confidence intervals (C.I.) are estimated using 100,000 draws from the estimated parameters distributions to simulate the path of core PCE price inflation going forward.
First quarter of forecast: 2022:Q2.
B. C.I. calculated using quasi-out-of-sample methods
Note: 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).
First quarter of forecast: 2022:Q2.