Skip to main content

The FRED® Blog

Christmas with FRED

Term deposits are deposits with specified maturity dates held by institutions eligible to receive interest on their balances at Federal Reserve Banks, but are separate and distinct from balances maintained in an institution’s master account at a Reserve Bank as well as from those maintained in an excess balance account. Term deposits are intended to facilitate the conduct of monetary policy by…

Ah, forget it!

Today, we aren’t really that interested in term deposits. We just wanted to draw a Christmas tree in a FRED graph.

How this graph was created: Search for this term deposits series and click “Add to Graph”; adjust the start date to May 2014; and under the “Edit Graph” menu, use the “Format” tab to change the line color and thickness. then, under the “Edit Graph” menu, go to the “Add Line” tab to the “Create user-defined line” option: Play with the coordinates (dates and y-axis values) to create the first line of the star. Continue to add user-defined lines as needed.

Suggested by Yvetta Fortova and Christian Zimmermann.

View on FRED, series used in this post: TERMT

Election surprises and exchange rates

Some election results are expected, and some come as a surprise. As people and markets adjust their expectations for the future with this new information, some financial variables may move around quite a bit.

The graph refers to two recent elections whose actual outcomes didn’t match forecasters’ expected outcomes: the British referendum vote on whether to stay in the European Union (a.k.a. Brexit) on June 23, 2016, and the recent U.S. presidential election on November 8, 2016. The red line shows the British pound per dollar exchange rate, and the blue line shows the Mexican peso per dollar exchange rate. Each series is centered on its respective election and indexed to 100 at the date of that election. (The interruptions in the lines show when markets were closed.)

In both cases, the value of the dollar relative to these currencies appreciated around 10% from the previous day’s value. This appreciation may reflect a negative (or less positive) revision to individuals’ expectations about the future of the British and Mexican economies based on the election outcomes.

How this graph was created: From the FRED homepage, search for and select the series “Mexico/U.S. Foreign Exchange Rate.” Then use the “Add Line” feature to search for and select “U.K./U.S. Foreign Exchange Rate” and add the series in dollars per British pound. To achieve a pounds-per-dollar series, under “Edit Line,” use the customize data section and type 100/a*100 in the formula box and click “Apply.” Finally. adjust the units of both series by selecting the “Index” option in the “Units” menu. Choose a custom date of 2016-06-23 for the pound and 2016-11-08 for the peso. Select the option to display integer periods instead of dates, and set the range to be -20 to 20 for all series.

Suggested by Max Dvorkin and Hannah Shell.

View on FRED, series used in this post: DEXMXUS, DEXUSUK

Price growth at the tails

When policymakers discuss the inflation rate, they’re referring to a measure of the “central tendency” of a distribution of price changes. There are many many many (many) prices in a developed economy. Here in the U.S., for example, we have maybe 20 types of sugar-coated flake-shaped cereal whose prices can change from one month to the next. So, to make the inflation rate meaningful, we must condense this distribution of prices to a measure of, as statisticians would call it, “central tendency.” However, reasonable people can differ on the proper measure because the distribution of price changes has long “tails.”

In short, the tail of a distribution is the part that’s farther away from the average. For example, we see evidence of the tail of the distribution of prices every morning when we pick up a coffee and a newspaper and drive into work: The prices of the first two items, like most other prices, change very slowly; but the price of gasoline fluctuates wildly from day to day. Certain categories—namely, food and energy—have larger swings than most other goods, so some prefer a price measure that looks at all goods except food and energy. This measure is called the “core” CPI. However, food and energy are not the only highly variable goods.

The top graph shows the ratio of mean CPI inflation to median CPI inflation.* The CPI measures inflation by choosing a basket of goods that are prominent among the average consumer’s purchases. Within this basket, the distribution of price changes is usually approximately symmetric, which we see because the ratio of the mean to median is usually about 1. (Actually, it’s slightly less, at about 0.9.) The interesting exception is during the Great Recession period, when commodity prices fell sharply, bringing a strong negative skewness for the first time since the mid-1980s. We can see this by looking at the bottom graph, which plots the ratio of mean core CPI to median CPI. Notice there is no negative spike in this measure of skewness. The Great Recession and its aftermath, however, show large changes in the “third moment.” In this period when the economy seemed to be in tremendous flux, the headline, average CPI moved little. However, the skewness—and the tails of the price distribution—changed quite a bit.

* This is not totally precise, because the change in headline CPI is not exactly the mean change in prices nor is median CPI exactly the median of the change distribution.

How to create these graphs: Top graph: Search for and select “median consumer price index” and “consumer price index for all urban consumers,” selecting “All items” and “Seasonally adjusted.” Chose “Percentage change” for the units in both. In the formula field, apply b/a. Bottom graph: Do the same, but instead of adding the “All items” consumer price index for all urban consumers, select “All Items Less Food and Energy.”

Suggested by David Wiczer.

View on FRED, series used in this post: CPIAUCSL, CPILFESL, MEDCPIM157SFRBCLE

Capacity utilization rate and the business cycle

The industrial capacity utilization rate is defined as the percentage of resources already installed or paid for by firms, such as capital and labor, actually used by corporations and factories to produce goods. This rate tends to move along with the business cycle: increasing during expansions, when companies are trying to produce more goods to meet demand, and declining during recessions, when demand for goods declines. And as the graph shows, the historical trend of total capacity utilization has been declining, as has real GDP growth.

Indeed, the average capacity utilization rate between 1967 and 1979 was around 84 percent, it declined to 81 percent between 1980 and 1999, and dropped down further to 77 percent between 2000 and 2016. Similarly, average GDP growth fluctuated around 3.3 percent between 1967 and 1999 and declined to around 1.9 percent in the period between 2000 and 2016.

How this graph was created: Select “Real GDP” from the “At a Glance” menu on the home page. Go to “Edit Graph” and under the “Add Line” panel search for “Capacity Utilization: Total Industry” and add it as a new line to the graph. Then, format Line 2 to be on the right y-axis position and change the line style to dash. Finally, select the desired date range.

Suggested by Maria Arias and Yi Wen.

View on FRED, series used in this post: A191RL1Q225SBEA, TCU

Employment in coal

Coal has been in the news lately—specifically, coal mining jobs. Let’s take a look at what FRED can show us about coal mining employment among U.S. states. For starters, half of U.S. states don’t mine coal. Of the 25 that do, 10 have only a handful of mines and many other states have so little employment in that industry that the Bureau of Labor Statistics doesn’t separate coal mining from other mining activities.* In the end, we have a precise data series for only three coal-producing states.

It turns out these three series are enough to show that coal mining employment has differed among states. Employment has trended down since the start of the series (in 1990) in both Kentucky and Pennsylvania. But Wyoming has no such downward trend; in fact, there’s been an uptick in recent years, except for a marked decrease in 2016. Why the difference? Coal from Pennsylvania and Kentucky has a much higher SO2 content, which is costly to remove during the burning process. So coal-fired power plants prefer to use the less-polluting coal from, say, the state of Wyoming (not to be confused with the coal-rich Wyoming Valley in Pennsylvania). But with the boom in shale gas extraction and the lower cost of natural gas (and other alternative fuels), even Wyoming coal is becoming less competitive. We’ll see what the future holds for this industry.

*Per the U.S. Energy Information Administration, the top five U.S. coal producers in 2015 were Wyoming, West Virginia, Kentucky, Illinois, and Pennsylvania. The BEA offers employment data for West Virginia and Illinois only for all forms of mining plus logging.

How this graph was created: Search for “coal employment” and select the three series. Click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: SMU21000001021210001SA, SMU42000001021210001SA, SMU56000001021210001SA

Subscribe to our newsletter

Follow us

Twitter logo Google Plus logo Facebook logo YouTube logo LinkedIn logo
Back to Top