Headline, Core and Supercore Inflation: Which Is the Fed’s Favorite?

Maintaining stable prices is a vital goal for any economy. When inflation, the general rise in price levels, spirals out of control, it throws economic decisions into disarray and makes life harder for everyone. To combat this, central banks rely on various measures to track and understand how prices are changing.

Three key indicators stand out: the Consumer Price Index (CPI), the Personal Consumption Expenditures Price Index (PCEPI), and the GDP deflator. The CPI, published by the Bureau of Labor Statistics, focuses on the cost of living for a typical urban consumer, tracking the price changes in a basket of goods and services. The PCEPI, from the Bureau of Economic Analysis, takes a broader approach, encompassing a wider range of goods and services. While both aim to capture the cost of living, the PCEPI is considered more responsive to changing consumer habits as its components are updated more frequently.

The GDP deflator, also from the Bureau of Economic Analysis, measures price changes across all goods and services produced within the US economy. While useful for assessing overall economic output, it doesn't directly reflect the cost of living for individuals.

The CPI is widely used for various purposes, like adjusting Social Security payments. However, the Federal Reserve favors the PCEPI for setting its inflation targets, considering it a more accurate reflection of overall price changes in the economy.

Beyond these "headline" inflation measures, economists often focus on "core" inflation, which excludes the more volatile food and energy sectors. By removing these fluctuations, core inflation provides a clearer view of underlying, persistent price trends.

Recently, "supercore" inflation, focusing on the price changes of services excluding housing, has gained attention as a potential indicator of deep-seated inflationary pressures.

The differences between these various measures offer valuable insights into the driving forces behind inflation. For instance, if headline inflation is lower than core inflation, it might suggest that falling food and energy prices are temporarily masking a broader inflationary trend.

Ultimately, managing inflation is a complex challenge. While understanding the nuances of different inflation measures is essential, the Federal Reserve's primary mission remains the same: to carefully control economic demand and maintain stable prices for a healthy and thriving economy.

What Is the Significance of Measuring Inflation at Different Frequencies

The importance and significance of measuring inflation at different frequencies include the following:

Capturing high-frequency price dynamics

Measuring inflation at higher frequencies like monthly or weekly allows for more timely tracking of price changes and inflation dynamics. This is crucial for policymakers to respond promptly to emerging inflationary pressures or shocks. High-frequency inflation data from sources like scanner datasets can provide more accurate and up-to-date inflation signals compared to lower frequency annual measures.

Monitoring short-term versus persistent inflation trends

Analyzing inflation across different frequencies helps disentangle transitory, high-frequency fluctuations from more persistent, lower-frequency inflation trends. This distinction is important for central banks to identify the underlying inflation pressures that may require a policy response versus temporary volatility that can be looked through.

Anchoring inflation expectations

Consumers' inflation perceptions and expectations can exhibit "frequency bias", being overly influenced by frequent price changes of certain goods like food and gasoline. Monitoring high-frequency inflation components helps central banks better understand how consumers form expectations and take actions to re-anchor expectations if needed.

Capturing sectoral inflation dynamics

Different sectors of an economy can experience varying inflation dynamics at higher frequencies based on sector-specific demand/supply conditions. Measuring inflation across sectors at higher frequencies provides valuable insights for policymakers on sources of inflation pressures.

Improving accuracy of inflation measurement

High-frequency price data allows statistical agencies to construct more accurate inflation measures by better accounting for new product introductions, quality changes, and consumer substitution behavior in real-time.

In summary, measuring inflation across multiple frequencies - monthly, quarterly, annually - provides a comprehensive view of price dynamics, helps identify persistent trends, informs policy decisions, manages inflation expectations, and ultimately leads to more precise and timely inflation signals for economic monitoring and policymaking.

What Are the Challenges of Measuring Inflation With High Frequency Data

Key challenges of measuring inflation with high-frequency data include:

• Handling product churn and volatility: High-frequency datasets like scanner data typically exhibit a large degree of product entry and exit as well as volatile movements in prices and quantities. This can lead to biases in the inflation measures if not properly accounted for.
• Lack of agreed methodology: There is currently no universally agreed upon method for calculating price indexes using high-frequency transaction data, reflecting a lack of systematic evidence on the performance of different index number approaches like bilateral vs multilateral methods.
• Conceptual challenges: Measuring inflation accurately is conceptually difficult even under ordinary circumstances. The unusual economic dynamics during the COVID-19 pandemic, such as shifts in consumer spending patterns and remote work trends, have further blurred the lines between consumer and business expenditures, challenging the efficacy of traditional inflation measures.
• Disentangling transitory vs persistent signals: High-frequency inflation data can capture transitory price fluctuations that may not reflect underlying persistent inflation trends relevant for policy decisions. Distinguishing between these transitory and persistent components poses a challenge.
• Anchoring inflation expectations: Consumers' inflation expectations can exhibit "frequency bias", being overly influenced by frequent price changes of certain goods. Managing these expectations requires carefully interpreting high-frequency inflation signals.
• Data limitations: While high-frequency data like daily prices improve inflation nowcasting, the limited data history for some indicators like weekly oil prices can constrain the estimation of their dynamic relationships with inflation over longer horizons.