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Time aggregation in FRED

In many instances, statistics are collected at a higher frequency than a user requires. In the example here, the unemployment rate is collected monthly, but we often have other labor market data collected annually. The question here is how to aggregate the high-frequency data into a lower-frequency statistic. In FRED, we have three options: average, sum, or end of period. In the graph, we compare annual unemployment data taking either the average over the year or the end-of-period observation. The choice of whether to use seasonal adjustment doesn’t affect the average. By definition, seasonal adjustment implies that December, the last month of the year, does not have a systematically different unemployment rate from any other month. However, averaging or summing will systematically give lower measures of variation than the end-of-period observation. The reason is simple, even without too much formal math: Suppose every month our observation is the annual number plus some monthly “noise” term. Either summing or taking the average, we essentially allow these monthly variations to cancel each other out. Taking an observation from the end of period includes all of the month-specific variation. In the graph, we can see that the red line, which takes annual unemployment as the final month’s observation, is more volatile. In fact, from 1979-2014, its coefficient of variation is 25.56%; the blue line, which takes the average, has a coefficient of variation of 24.86%.

How this graph was created: Search for “unemployment” and select the seasonally adjusted civilian unemployment rate. Using the pull-down menu, change “Frequency” to “Annual.” The default “Aggregation Method” is “Average,” and we will keep that. Then, “Add Data Series” and again search for “unemployment.” Add a new series using “unrate,” the same data as last time. Again, change it to an annual frequency. But this time, change the aggregation method to “End of Period.”

Suggested by David Wiczer.

View on FRED, series used in this post: UNRATE

Federal funds rate: target vs. reality

The traditional policy tool of the Fed is to target the federal funds rate. Note the term target. Indeed, the Fed does not set this interest rate; rather, it sets the target and then conducts open market operations so that the overnight interest rate on funds deposited by banks at the Fed reaches that target. Obviously, reaching the target is sometimes harder to do, especially in times when there’s a lot of uncertainty in the markets. The graph above compares the target (or target band more recently) with the effective federal funds rate. While the two coincide quite well over most of the 10-year period, there are important deviations that correspond to various financial market events. Nevertheless, these deviations are short-lived, which shows that the open market operations do have the desired effect.

How this graph was created: Search for “federal funds rate” and these four series should be among the top choices. Select the daily rates and use the “Add to graph” button to add them to the graph.

Suggested by Christian Zimmermann

View on FRED, series used in this post: DFEDTAR, DFEDTARL, DFEDTARU, DFF

The many faces of the federal funds rate

It’s no surprise FRED has federal funds rate data. But these data aren’t as simple as you may think. They have changed form over time as the Federal Open Market Committee has changed the way it sets the funds rate: From 1982 through 2008, the target rate is a discrete number. For example, it is 9.5% on Oct. 1, 1982, 3% on Oct. 1, 1992, and 1.75% on Oct. 1, 2002. At the end of 2008 (i.e., since the financial crisis), the FOMC began setting a target range of 0.00 to 0.25%. And, to further complicate matters, the data prior to 1994 come from the working paper “A New Federal Funds Rate Target Series: September 27, 1982 – December 31, 1993,” making it an altogether different series.

The discrete-target funds rate for 1982-2008 is DFEDTAR in FRED. The target-range funds rate since then has a lower and upper bound—DFEDTARL and DFEDTARU, respectively.

Of course, FRED will continue to accommodate changes to the funds rate. As the U.S. economy overall and employment specifically have recovered, the FOMC has signaled a need to respond by changing the rate. And financial observers around the globe are anxious about how the FOMC will respond. If at some point in the future the FOMC moves from a target range to a discrete target, FRED will also need to respond: In this case, the FRED team plans to change the lower-bound and upper-bound series to a commensurate data point to solve this issue. This method will ensure that the history of the range remains intact, while allowing FRED users to present the data in the simplest way possible. We will not combine the series, create a new series, or update the DFEDTAR series.

How to make this graph: The FRED Team prefers to present these data by creating one graph with the three aforementioned series. First search for and add DFEDTAR to a graph. Next use the “Add Data Series” menu below the graph to search for DFEDTARL and DFEDTARU in the field that asks you to “Type keywords to search for data.” Select these series and add them to the graph with the “Add Series” button.

Suggested by Travis May.

View on FRED, series used in this post: DFEDTAR, DFEDTARL, DFEDTARU

A bit of religion in the dismal science

FRED’s main contribution to the “dismal science” of economics is its core economic and monetary data. But FRED recently added some socio-demographic indicators as well. None of these indicators covers religion, per se; but quite a few relate to religion indirectly. A recent search for “religion” in FRED yielded 185 results, and two of those series are highlighted above: (i) real private consumption expenditures dedicated to religion and other social services and (ii) real investment in religious nonresidential structures, which we presume are mostly churches. (Both series are chain-type indexes.) For comparison, we also include real GDP in the graph, with all indexes having a value of 100 in 2009. Religious consumption expenditures (which may have a variable non-religious component) have tracked GDP quite well since 1929, but church building has plummeted over the past ten years. This decline predates the construction industry’s overall decline during the previous recession. Thus, there may be non-economic factors at play here.

How this graph was created: Search for “religion” and narrow the results by clicking on the “nation” tag. You should find the first two series fairly quickly. Again: The “religious” series are chain-type indexes. Select them and click on the “Add to graph button.” Then add the “real GDP” series and change units to “Index (Scale value to 100 for chosen period)” to 2009-01-01.

Suggested by Christian Zimmermann

View on FRED, series used in this post: C309RA3A086NBEA, DSOCRA3A086NBEA, GDPCA

More about comparing oranges

Our previous post was about comparing apples and oranges. This post takes a different approach and searches FRED for just oranges. Most of the results have nothing to do with fruit, but rather are economic indicators for Orange County, California. FRED houses many regional data series, and if you look you’ll see there are seven other Orange Counties in the U.S. We compare four in the graph by looking at their unemployment rates. Indiana’s O.C. stands out, with a high and highly fluctuating rate; it is small and poor, with employment dominated by a large casino and golf resort. (Note: None of these series are seasonally adjusted.) Vermont’s O.C. is a little larger and better diversified, so it has smaller fluctuations. California’s O.C. is the largest and also has small fluctuations. North Carolina’s O.C., home of the University of North Carolina flagship campus, is of special interest, as its unemployment rate jumps up between 1999 and 2000. This could be due to a reclassification or a mass layoff. Maybe a reader knows why…

How this graph was created: Search for “Orange County unemployment,” select the counties you want to graph, and click on “Add to graph” to do so.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CAORAN7URN, INORURN, NCORAN2URN, VTORAN7URN

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