Federal Reserve Economic Data: Your trusted data source since 1991

The FRED® Blog

Is this the new fastest economic recovery?

In an earlier post, the FRED Blog compared economic activity during the COVID-19-induced downturn and recovery across the G-7 countries: the U.S., the U.K., Japan, Canada, France, Germany, and Italy. Today, we focus on the U.S. and compare activity during the 2020-2021 downturn and recovery with activity during past episodes.

The FRED graph above plots the value of quarterly real (i.e., adjusted for inflation) GDP during and after the five most recent economic recessions: 1981-1982, 1990-1991, 2001, 2007-2009, and 2020-2021 (red dashed line). The billions of dollars reported by the U.S. Bureau of Economic Analysis are plotted as a custom index. The index has a value of 100 at the start of each recession, which is marked as the zero “date” on the left-hand side of the graph. Each period to the right of that “date” represents one quarter afterward.

In the 2001 recession, real GDP did not decrease. But in all the other economic contractions, real GDP fell below its pre-recession level for several quarters. The other recessions took more than a year to bounce back; but one year after the onset of the COVID-19-induced recession, U.S. real GDP is already above its pre-recession value.

So, is this the fastest recovery in overall economic activity on record? Most likely, yes.

In a previous post, we used annual data to compare economic recoveries since 1937. But quarterly real GDP data aren’t available prior to 1947, so we can’t directly compare the episodes in the graph above with the 1937-1938 episode in the graph we produced a year ago. Still, a year after the 1937-1938 recession started, real GDP was 3.7% below its pre-recession value. And four quarters after the COVID-19-induced recession started, real GDP was already 0.4% above its pre-recession value.

If you follow the FRED Blog, you already know that over the past year different sectors of economic activity have expanded and contracted at different rates. Keep in touch with the blog and learn more about the shape of the ongoing recovery over the next few months.

How the graph was created: From a previous blog post: Search FRED for “real gross domestic product” and select the series with the ID “GDPC1.” Add the same series to the graph four more times. Next, change the units to “Index (Scale value to 100 for chosen date)” and use the expanded menu to select the date to which you’d like to index each series. From the U.S. recession menu, select these dates for the five series: 1981-07-01; 1990-07-01; 2001-03-01; 2007-12-01; and 2020-02-01, the start dates of the early 80s recession, early 90s recession, early 2000s recession, the Great Recession, and the COVID-19-induced recession (according to the NBER), respectively.
For each series, check the “Display integer periods” box. The x-axis will show integers as time periods instead of dates. The base period is shown as 0: Negative numbers represent periods (quarters, in this case) before the base period, and positive numbers represent periods after the base period. Change the start integer to 0, so the graph begins at the start of each recession. Change the end integer to 8, so the graph ends 8 quarters after each recession started. Finally, to use the same graph style shown here, select the circle option under “Mark Type.”

Suggested by Diego Mendez-Carbajo.

Measuring uncertainty and volatility with FRED data

Uncertainty and volatility are closely related but distinct concepts. People are uncertain if they lack confidence in their knowledge of the state of the world or future events. News is more likely to change the views of people with high uncertainty. In financial markets, changing views is associated with changing asset prices. Volatility denotes the size of changes in asset prices, so volatility is an ex post (after the fact) measure of uncertainty.

Uncertainty and volatility are carefully watched variables because of their relation to financial crises. During such periods, uncertainty often rises to high levels as the prices of risky assets, such as stocks, tend to fall. This produces a short-term, negative relation between uncertainty and returns.

FRED has a number of series that are related to uncertainty and/or volatility, some of which are derived from options data. One of the most frequently used such series is the Chicago Board of Options Exchange (CBOE) Volatility Index, or VIX. This options-derived series predicts one-month-ahead volatility on the CBOE S&P 500 futures contract.

To illustrate an example of the common negative relation between uncertainty and stock returns, FRED users can employ the two-axis option to overlay a volatility index, such as VIX, with the underlying price series. In the first FRED graph at the top of the post, one can easily see how the rise in VIX coincides with the decline in S&P 500 prices during the period from mid-February to late-March 2020.

Note that if users graph a long span of daily data, such as VIX or the S&P series, FRED will automatically revert to graphing lower-frequency data instead of daily data, although the underlying series are still daily. For example, if we extend the time scale on the first graph back to 1990, FRED will revert to graphing monthly data and no longer show us the values for individual days. See the second FRED graph above.

Users can define their own lines to enhance the look of their graphs. Horizontal lines can emphasize periods when uncertainty or volatility is above a certain level. One could download a series to a spreadsheet or some statistics software to find its 10th or 50th or 90th percentile and then use those percentile values to emphasize particularly high or low values. For example, in the third graph shown above, 90% of the VIX series observations are less than 28.84. By creating a line at that level, we can indicate the upper 10% of VIX observations.

Users can also create a vertical line to highlight action on dates of interest. For example, March 16, 2020, was a very volatile day for U.S. financial markets, as participants came to grips with the impact of COVID-19 and the likely policy responses. The vertical line in our very first graph draws emphasis to this date as a turning point in the financial market reaction to COVID-19.

Finally, it’s possible to compare an ex ante (before the fact) prediction of volatility over the next month (e.g., VIX) to an ex post (after the fact) volatility measure that uses daily prices. To compute actual daily volatility (i.e., the absolute daily percent changes), change the units of S&P 500 prices to percent change and apply the following formula in the formula bar: (a^2)^(1/2). Ex post volatility over a day need not exactly correspond to ex ante predictions of volatility over a month. For example, volatility may be low today but high over the next month if important news is expected to come out in a couple of weeks. Conversely, volatility might be temporarily high as important news is released but expected to decline in the near future. Still, our last graph (below) usefully illustrates the tendency of the two series to move together.

How these graphs were created:
First graph. Search FRED for “VIX” and select “CBOE Volatility Index: VIX.” The default graph will be a daily graph of VIX. Use the “edit graph” button to open the editing box. Use the “add line” tab to search for and add the daily series “S&P 500.” Use the “format” tab to shift the y-axis position of the “S&P 500” series to “right axis.” Go back to the “add line” tab to “Create user-defined line.” Click on “Create line” and type in starting and ending dates of “2020-03-16” and starting and ending values of 0 and 90. This will produce a vertical line on 2020-03-16. You can change the line style, width, and color from the format tab. Returning to the main graph, use the date range boxes to set the beginning date to “2019-06-10.”
Second graph. From the first graph, change the beginning date to “1990-01-02” using the date range boxes.
Third graph. Search FRED for “VIX” and select “CBOE Volatility Index: VIX.” Use the date range boxes to set the beginning date to “1990-01-02”. Use the “download” button to download the series to an Excel file. Once you have opened the file, type: =PERCENTILE.EXC(IF(ISNUMBER(B13:B8214),B13:B8214),0.9) in an empty cell (cell range will be different from when this was written). This returns the 90th percentile of the VIX data (i.e., 28.84). Back to the graph, use the “edit graph” button, go to the “add line” tab to “Create user-defined line.” Set the starting and ending values for the line to “28.84.”
Fourth graph. Search FRED for “S&P.” The default graph will be a daily graph of the S&P 500 for the past 5 years. Click the “edit graph” button and select the units for the S&P 500 as “Percent Change” and the formula as (a^2)^(1/2). Click on “add line” and search for “VIX”. “Select the daily “CBOE Volatility Index: VIX”. Select “Add data series.” Select the units as “Index,” not percent change. Using the format tab, select “Right” for the y-axis position of “CBOE volatility Index: VIX” series. Close the editing box. Using the date range boxes for the graph, select a 5-year date range.

Suggested by Christopher Neely.

The pandemic’s boost to online sales: A one-time event or a new normal?

The FRED graph above shows online retail sales. It’s no surprise these sales have been steadily increasing, even if there are a few rough patches during recessions. And it’s also completely expected that the pandemic provided a large boost to e-commerce over on-site retail. The question is whether this is a temporary boost that will subside once the world returns to normal. That is, will the previous trend continue where it left off or has the online sector gotten a boost that will put it on a higher trajectory?

Obviously, it’s still too early to say exactly what the “new normal” will look like. But, at the time of this writing, it looks like the second option is correct and there’s a new trajectory for online sales. But not much has been normal about the current recession, so only time will tell. Visit this blog post later to see how the graph updates with new data.

How this graph was created: Search FRED for “online retail” and the right series is among the first choices.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: ECOMSA

Subscribe to the FRED newsletter

Follow us

Back to Top