Federal Reserve Economic Data

How certain are you of what’s going to happen over the next few months? People’s confidence in anticipating the future dwindles during periods of major economic change. Economists and analysts try to gauge the level of uncertainty about economic conditions because it can affect economic decisions (saving, spending, investing, switching jobs…), which can affect aggregate economic outcomes.

Our first FRED graph above plots one widely used measure of uncertainty: the CBOE Volatility Index, better known as VIX. A post from February explains the VIX in detail. Basically, it uses stock price options to measure how much volatility financial markets expect in the near future, with volatility serving as a proxy for economic uncertainty. This volatility index rose sharply between March and April 2025 and is nearing levels last seen during the COVID-19 pandemic.

Many factors can cause uncertainty, such as structural economic shocks, global events, and financial crises. Policy actions can also drive economic uncertainty. In our second FRED graph above, we use the overall economic policy uncertainty index (EPU) to understand how important policy uncertainty is for the recent increase in overall uncertainty.

The EPU focuses on the economic effects of government policies, rather than financial and economic conditions. It measures this uncertainty from news coverage from a large set of US newspapers. For example, an uncertainty value of 120 would mean that there’s 20% more uncertainty about economic policy than is typical during the base period.

Economic policy uncertainty rises during periods of economic turmoil: It spiked in 1998 during the Asian financial crisis, in 2008 at the onset of the Great Recession, and in 2020 as COVID pandemic lockdowns began. The EPU index began rising persistently in late 2024.

The EPU also has values for subcategories such as taxes, regulation, and trade. By looking at these subcategories, we can identify the specific types of economic policy that are most associated with uncertainty.

Our last FRED graph above plots values for several subcategories as well as overall EPU. Until summer 2024, most types of economic policy uncertainty were at or below 100, meaning they were at normal or low levels. Recent uncertainty, which began rising in 2024, has been especially pronounced for trade policy (pink dashed line). By February 2025, it had jumped to almost 2500, which implies close to 25 times more uncertainty than the norm.

Trade policy uncertainty has spiked before: prior to the signing of NAFTA in late 1993, peaking at just over 1000 before returning to baseline values, and in 2018-19 as China and the US imposed additional tariffs on each other. The most recent increase in trade policy uncertainty corresponds to the announcements of new tariff policies by the Trump administration throughout early 2025. In general, trade policy uncertainty rises during periods of major change in trade policies but returns to lower levels once those policies are established.

How this graph was created: Search FRED for and select the VIXCLS (CBOE Volatility Index) series. For the second graph, search for and select the EPU (economic policy uncertainty) series. For the third graph, search for and select the EPU categorical index, overall series. Then scroll down and click on the release table, below the graph, where you can choose the categories you want to display. Note that this post will continue to be updated with more recent data after its publication.

Suggested by Miguel Faria-e-Castro and Marie Hogan.

In times of market turmoil, fear and uncertainty take center stage. One tool analysts use to measure this fear is the VIX, often called the “Fear Index,” published by the Chicago Board Options Exchange (CBOE).

The graph shows that the VIX value is about 20 on average but much higher during periods of extreme uncertainty, such as the onset of the COVID-19 pandemic in 2020 and the financial crisis in 2008:

• In 2020, the end-of-month peak was 53.54 in March and the daily peak was 82.69 March 16.
• In 2008, the end-of-month peak was 59.89 in October and the daily peak was 80.86 November 20.

But what exactly is the VIX?

VIX, or volatility index, is a forward-looking measure of expected future volatility in the stock market. It captures these expectations using prices of “out of the money” (OTM) put and call options on the S&P 500 index. These options are particularly useful in capturing future expectations of extreme price movements. For example, OTM put options help protect against downside risk and become more expensive when investors anticipate a decline in the S&P 500. The VIX calculates a weighted average of implied volatilities across a range of strike prices for these options, providing an estimate of expected volatility over the next 30 days.

The FRED graph shows the VIX’s countercyclical pattern over time: It rises during economic downturns and falls during booms. Why? For example, in recessions, firms borrow more and thus their stock returns could become increasingly volatile. Or investors could become more risk averse and act accordingly. Also, high uncertainty during recessions can potentially further exacerbate these economic downturns.

The VIX, as a barometer of market uncertainty, reflects the collective expectations of investors about future stock market volatility. it is clearly associated with periods of economic turmoil, but it also highlights the natural cycles of confidence and caution in financial markets.

How this graph was created: Search FRED for “VIX” and you’ll have the option to select the “CBOE Volatility Index: VIX” series, with series ID “VIXCLS.”

Suggested by Aakash Kalyani.

Measures of inflation are some of the most popular data series on FRED. Two of the most important ones are the consumer price index (CPI), constructed by the Bureau of Labor Statistics (BLS) in the Department of Labor, and the personal consumption expenditures price index (PCE), constructed by the Bureau of Economic Analysis (BEA) in the Department of Commerce.

The CPI is probably the most widely watched measure of inflation and is used for many purposes, such as indexing Social Security payments. The Federal Open Market Committee (FOMC) has been looking at the PCE price index since the 1990s, however, and made that index the measure for its official inflation target in January 2012, when they introduced an official target.

To understand how the CPI and PCE inflation rates differ, consider a stylized representation of a price index for the year 2023 as the weighted sum of the prices of three types of goods, which we will call goods 1, 2, and 3.

P₂₀₂₃=w_1,2023 p_1,2023+w_2,2023 p_2,2023+w_3,2023 p_3,2023

The weights, w, represent how much money is spent on each good in the consumption basket. For example, gasoline would get a higher weight in a consumer price index than shoelaces. As relative prices, technology, or people’s tastes change, the weights in the baskets change.

Both the CPI and PCE are constructed in this general way, as the weighted averages of prices of various goods, but they differ.

1. The two price indices measure somewhat different baskets of goods: The CPI is designed to measure the cost of living for an urban consumer, while the PCE measures a broader cost of living. Because of this difference in emphasis, the weights in the baskets differ and the CPI famously places more emphasis on the cost of housing.
2. The weights in the CPI basket aren’t revised as often as those in the PCE basket, meaning that the PCE probably better measures consumer responses to rapidly changing relative prices.

The FRED graph above shows that monthly CPI (blue line) and PCE (red line) inflation move closely together, but the CPI generally exceeds the PCE. Over the full sample since 1960, the arithmetic average of 12-month CPI inflation was 3.77% and the standard deviation—a measure of volatility— of that series was 2.83%. The analogous figures for the PCE price index inflation were 3.31% and 2.43%. That is, the CPI inflation rate was 0.46% higher on average and somewhat more volatile. The fact that the PCE weights are revised more often than the CPI weights helps explain the higher average CPI inflation because consumers tend to substitute away from products whose prices rise sharply and the PCE index more quickly reflects such behavior.

The purple line on the FRED graph shows the difference between the CPI and PCE inflation rates, with an average value of 0.46%. This difference is almost always positive but small, usually in the range of 0 to 1%, but it does increase with overall inflation rates.

One component that may also help depict the difference between these two price indexes is annual inflation in “imputed rental of owner-occupied housing.” Shown by the green line, this is basically what a homeowner would have to pay to live in the house if they were renting it. Since CPI has higher weights for housing, this imputed rent should contribute much more to the CPI than the PCE. And it does seem to be correlated with the overall difference between the CPI and PCE inflation rates (purple line), but saying any more would require more careful analysis.

How this graph was created: On FRED, search for and select “CPI.” From the “Edit Graph” panel, change “Units” from “Index” to “Percent Change from Year Ago.” With the “Add line” option, search for “PCE” and select “Personal Consumption Expenditures: Chain-type Price Index,” then click “Add data series.” Repeat for “imputed rent,” selecting “Imputed rental of owner-occupied housing.” Add a line again, with the CPI and PCE series and apply formula a-b. Toward the top of the editing box, select “Percent Change from Year Ago” in the “Units” box and select “Copy to all.” Adjust the sample period to start on 1960-01-01.

Suggested by Christopher Neely.