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Interest rates for future centenarians

Discount rates for evaluation of future values

FRED recently added high quality market bond yield curve data from the U.S. Treasury. These are interest rates computed from high-quality commercial bonds to reflect the market’s thinking about how much it’s discounting future incomes with minimal risk. The U.S. Treasury needs these measures to evaluate the current value of liabilities in pension funds. You do this properly by using various maturities—from 6 months to 100 years in 6-month intervals. This produces an interesting yield curve. We focus here on the 100-year example. Obviously, there’s no commercial bond out there with a 100-year maturity right now. The calculation intrapolates for the various maturities and in this case likely extrapolates. We’re wondering, though, how a 100-year discount rate rate could be useful for pension liability pricing, as no employee alive today would reasonably expect to receive a pension distribution a century from now. However, this can be useful for other purposes, such as evaluating the usefulness of infrastructure with long lifespans or the impact of climate change. Note also that this 100-year rate has been decreasing significantly, just as all the others have, showing that the current interest rate environment has an effect far into the future.

How this graph was created: Search for “HQM bond” and, surprisingly, the 100-year rate is among the top choices.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: HQMCB100YR

Going postal

Plotting your data to test seasonal adjustment

Ever take a statistics class? If so, do you recall your instructor telling you to “plot your data” and look at it before using it? It’s sound advice but is not always heeded, which can lead to foolish data-driven errors. While surfing through FRED (as I am wont to do; and who isn’t?!) I found such an error related to the difficulties in seasonal adjustment.

The graph shows two series: seasonally adjusted (red) and not seasonally adjusted (blue) U.S. postal employment. There’s a clear increase in employment each December to help cover the Christmas rush. Since the December rise is temporary and conveys no long-term information about employment patterns, we usually look only at the seasonally adjusted figures. But as we can see in the red line, the seasonal adjustment methodology only partially removes the seasonal pattern. In most cases, it does a good job removing the December effect between the early 1970s and now. But in the earlier years, the seasonal adjustment fails spectacularly, leaving the bulk of the December effect uncorrected. Clearly, the pattern of seasonality has changed across time and the methodology used to seasonally adjust should reflect that change—and there’s no way we would have known that had we not “plotted the data” before making the adjustment.

How this graph was created: Browse data by category. Under the category “Population, Employment, & Labor Markets” select “Current Employment Statistics (Establishment Survey).” Then select the subcategory “Government.” Scroll through the 23 series to find “All Employees: Government: U.S. Postal Service” and select the not seasonally adjusted version. From the “Edit Graph” option, use the “Add Line” tab to search for “postal service employees.” Select the seasonally adjusted version of the series “All Employees: Government: U.S. Postal Service” and click “Add data series.”

Suggested by Michael McCracken.

View on FRED, series used in this post: CES9091912001, CEU9091912001


When prices go out of control

You might guess that hyperinflation refers to increases in the price level at very high rates. There’s no official threshold for “very high,” but the four cases shown in the graph are clearly examples of hyperinflation. The most spectacular case in FRED is Zimbabwe: The data are incomplete because in 2009 the country actually stopped reporting inflation, which likely rose several orders of magnitude higher that year. First, to be clear, Zimbabwe’s inflation rate of 24,000% means that prices were multiplied by 241 within a year. Peak hyperinflation in the other three countries has a factor of 42, 32, and 30. Second, why does hyperinflation occur? Generally, it’s because the government takes over the central bank and finances its operations by printing money. As people see this occurring, money loses value and the government has to print even more to stay afloat. This vicious circle can then be broken only by a radical change of practice: In Zimbabwe, that was abandoning the use of the local currency, which is indeed radical, as the country reported deflation in some years since going cold turkey. (Turkey, by the way, also has had periods of very high inflation, but not as dramatic as our examples. We reported on this earlier.)

How this graph was done: Search for “Zimbabwe inflation.” Once you have the graph, use the “Edit Graph” option to open the “Add Line” tab to search for other inflation rates. Repeat until satisfied.

Suggested by Christian Zimmermann.


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