We aim to provide a measure of inflation’s trend, or “persistence,” and identify where the persistence is coming from.

Our interactive charts report monthly MCT estimates from 1960 to the present. We also provide estimates of how much three broad sectors (core goods, core services excluding housing, and housing) are contributing to overall trend inflation over the same time span. We further distinguish whether the persistence owes to common or sector-specific components. Data are available for download.

We update the estimate of inflation persistence and share sectoral insights following the release of PCE price data from the U.S. Bureau of Economic Analysis each month.

A dynamic factor model with time-varying parameters is estimated on monthly data for the seventeen major sectors of the PCE price index. The model decomposes each sector’s inflation as the sum of a common trend, a sector-specific trend, a common transitory shock, and a sector-specific transitory shock. The trend in PCE inflation is constructed as the sum of the common and the sector-specific trends weighted by the expenditure shares.

We use data from all seventeen of the PCE’s sectors; however, in constructing the trend in PCE inflation, we exclude the volatile non-core sectors (that is, food and energy). The approach builds on Stock and Watson’s 2016 “Core Inflation and Trend Inflation.”

To make it easier for users to better understand and replicate our results, we provide MATLAB code for the model. The code, along with a snapshot of the PCE data set, can be found on GitHub. The newest releases for all data series are publicly available from the BEA or the FRED database.

The core inflation measure simply removes the volatile food and energy components. The MCT model seeks to further remove the transitory variation from the core sectoral inflation rates. This has been key in understanding inflation developments in recent years because, during the pandemic, many core sectors (motor vehicles and furniture, for example) were hit by unusually large transitory shocks. An ideal measure of inflation persistence should filter those out.

BEA monthly revisions as well as other BEA periodic revisions to PCE price data do lead to reassessments of the estimated inflation persistence as measured by the MCT estimates. Larger revisions may lead to a more significant reassessment. A recent example of the latter case is described on Liberty Street Economics in “Inflation Persistence: Dissecting the News in January PCE Data.”

Historical estimates in our MCT data series back to 1960 are based on the latest vintage of data available and incorporate all prior revisions.

The MCT model adds to the set of tools that aim at measuring the persistent component of PCE price inflation. Some approaches, such as the Cleveland Fed’s Median PCE and the Dallas Fed’s Trimmed Mean, rely on the cross-sectional distribution of price changes in each period. Other approaches, such as the New York Fed’s Underlying Inflation Gauge (UIG), rely on frequency-domain time series smoothing methods. The MCT approach shares some features with them, namely: exploiting the cross-sectional distribution of price changes and using time series smoothing techniques. But the MCT model also has some unique features that are relevant to inflation data. For example, it allows for outliers and for the noisiness of the data and for the relation with the common component to change over time.

The MCT model provides a timely measure of inflationary pressure and provides insights on how much price changes comove across sectors.