Federal Reserve Economic Data

The FRED® Blog

What’s up (or down) with the yield curve?

Analyzing the new most-popular series in FRED

For as long as we can remember, the most popular series in FRED has been the consumer price index (CPI). Well, not anymore. Recently, the series describing the difference between the 10-year and 2-year Treasury constant maturity rates became the most popular. Why this sudden interest? It has to do with the concept of the yield curve: Under normal circumstances, long-term interest rates are higher than short-term interest rates (when annualized), principally because the long term is usually perceived as riskier and so long-term debt demands a higher return. Again, normally, if you plot the interest rates at different maturities, you get an upward-sloping (yield) curve. But if for some reason the short term becomes unusually risky, the curve (or portions of it) may become downward sloping. And why is that important? The graph makes it clear that this kind of yield curve inversion has been associated with impending recessions. (See the gray vertical bars.) As the yield curve gets close to such a situation, there’s going to be a lot of interest in it.

How this graph was created: From the FRED homepage, open the tab “Popular Series,” click on the first one (at the time of this writing, anyway), and expand the sample to the maximum.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: T10Y2Y

If they drive, they will park (Or if they park, they will drive?)

Correlation does not always equal causation

This graph shows that the more people drive, the more they park and generate revenue for parking lot and garage operators. While there’s clearly a correlation between these two indicators, it isn’t clear that there’s a straightforward causality between them. In fact, a third indicator may be affecting the other two: the number of cars in use, the size of the road network, economic activity in general, commuting distance… Or maybe it’s a combination of all or some of these. This ambiguity is what makes statistical analysis much more complex than simply looking at correlations in a graph. FRED helps you stay rigorous by allowing you to download data into your favorite statistical software, either with a download from FRED itself (for example, via the “Download Data” link below the graph) or natively from the software of your choice. For starters, you can use this published data list.

How this graph was created: Search for and select “parking lot revenue” and click on “Add to Graph.” From the “Edit Graph” menu, search for “GDP deflator” in the “Customize data” section and add the series, applying formula a/b. Then from the “Add Line” tab, search for and add “vehicle miles.” Finally, from the “Format” tab, place the y-axis of the second line on the right side.

How this data list was created. For starters, you need to (create and) log on to a FRED account. Then, from any account page, click on “Add new” and select “Data list.” Give it a name. Then search for the series, check the series you want, and click on “Add to data list.” Repeat until satisfied. You can make the data list public and will be required to give it a public name.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: GDPDEF, REVEF81293TAXABL, TRFVOLUSM227NFWA

Hyperinflation in Venezuela

Using exchange rates to measure out-of-hand inflation

There’s inflation and then there’s hyperinflation, which is when inflation gets out of hand. There’s no official definition, but economists tend to use the “if it looks like hyperinflation, then it is” dictum. When price changes occur rapidly—say, several times within the same day—that’s hyperinflation. When bank notes don’t have denominations large enough to make payments easy, that’s hyperinflation. And measuring hyperinflation isn’t easy, as no statistical office can keep up with the rapid changes in price. One way to track this phenomenon, though, is to look at exchange rates.

The first graph shows the exchange rate between the Venezuelan bolívar fuerte and the U.S. dollar. Quite obviously, something out of the ordinary happened. The bolívar lost value rapidly—so much so that the graph allows us to see only a few recent data points.

[ Update 8/20/2018: The Venezuelan government announced economic policies designed to stem hyperinflation. The immediate impact is to raise the exchange rate to 6 million bolívars to the U.S. dollar (up from about 200,000). Time will tell whether these policies are successful. ]

One remedy for visualizing the wide range of values is to use a log scale, as the second graph does. A graph with a log scale will show with a straight line any data that increase at a constant rate. If the data increase at an increasing rate, the line moves steeper (i.e., it becomes convex). A few things are remarkable. First, there have been long periods of constant exchange rates, owing to the government’s policy of setting those rates. (See the several straight lines.) But recently, the rise in the exchange rate has been accelerating. (See the several steps up and eventually the vertical line.) This behavior in the data is characteristic of hyperinflation, which is obviously not sustainable.

How these graphs were created: For the first graph, search for and select “Venezuela exchange rate” (the monthly series) and click on “Add to Graph.” For the second graph, adjust the first graph in the “Edit Graph” section: Use the “Format” tab to select “Log scale” on the left.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: EXVZUS


Back to Top