Federal Reserve Economic Data

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Negative interest rates

On June 11, 2014, the European Central Bank broke new ground by lowering one of its key policy rates below zero. That is, the rate in question (the rate on the deposit facility available overnight to European banks) is now negative. While a policy rate can in principle be set at any level, it is more difficult to think about a market interest rate that would be negative. Indeed, one would always earn a higher return by simply holding on to one’s money rather than depositing it for a negative return. Yet, the graph shows two rates—one in Switzerland and one in Denmark—that are in negative territory.

What’s special about Switzerland and (more recently) Denmark? The Swiss franc has a reputation as a very stable currency and hence acts as a refuge currency when trouble is brewing elsewhere. Given that Switzerland is a small economy, when the Swiss franc is high in demand worldwide, investors are willing to accept negative rates if they think their own local currencies may depreciate. This happened when the fixed exchange rate regime of Bretton Woods was in jeopardy, later when European currencies were volatile, and recently when the European Monetary Union went through some pains and few non-euro currency options were available. One of those currencies was the Danish krone, which then also found itself in the role of a refuge currency.

How this graph was created: Search for “immediate rates,” select the relevant countries, and click on the “Add to graph” button.

Suggested by Christian Zimmermann

View on FRED, series used in this post: IRSTCI01CHM156N, IRSTCI01DKM156N

Output volatility in small and large countries

The best investment advice is to diversify your asset portfolio because it reduces the volatility and risk of the portfolio. The same applies to the economic performance of countries. The better diversified they are in terms of sectors, the less they suffer from large economic fluctuations. (This concept applies when all other factors are equal, of course; we have recently seen that emerging economies suffer from large fluctuations.) So, how to illustrate the benefit of diversification? One way is to contrast a large country such as the U.S., which covers virtually every imaginable sector, with smaller countries whose size limits the number of industries they can have. The graph shows per-capital real GDP growth for the U.S. (thick black line) and for three countries whose combined population amounts to about 3.5% of the U.S. population. It is quite easy to see that U.S. GDP growth fluctuates less.

How this graph was created: Search for “Constant GDP per capita” for the various countries and add those series to the graph. Transform each series to “Percentage change” and emphasize the line for the U.S. so it stands out (in this case, it is thicker and black).

Suggested by Christian Zimmermann

View on FRED, series used in this post: NYGDPPCAPKDDNK, NYGDPPCAPKDLUX, NYGDPPCAPKDSGP, NYGDPPCAPKDUSA

Spurious correlation

Relationships between macroeconomic time series are not usually straightforward enough to establish with a simple graph. The problem is that almost all time series tend to grow in the long term as an economy grows. So, any measure in nominal terms will grow even more, since inflation rates are almost always positive. Because time series can exhibit a common trend, it becomes difficult to interpret whether there is a relationship between them beyond that common trend. We call this spurious correlation. There are various ways one can isolate the common trend, and we show some here using M2 and total federal debt. Above, with just the raw series, all we can see is that they both tend to increase in the long run at roughly the same rates.

In the second graph, we simply take growth rates of both series. Now the trend is gone, and it is much more difficult to argue that there is some correlation here, positive or negative. (Remember also that correlation does not mean causation: Even if we saw some relationship, we wouldn’t be able to tell whether one series is affected by the other. That requires more substantial statistical analysis.)

In the third graph, we remove the trend in another way: by dividing each series by another series that also has this trend. In this case, we take nominal GDP: GDP because it measures the size of the economy, and nominal because both M2 and the federal debt are measured in nominal terms. The picture of the two ratios now looks different, but it is still difficult to claim that there is a systematic relationship between them. Looking only at the first graph, one would not have concluded that.

How these graphs were created: Search for “M2” and “federal debt” to find the series: Be sure one of the series has its y-axis on the right. For the second graph, select “Percent change from year ago” for both series. For the third graph, change units to levels and add “Gross Domestic Product” to “M2” and apply the transformation “a/b”; then replace federal debt with the debt/GDP ratio available in the database (or create that ratio yourself).

Suggested by Christian Zimmermann

View on FRED, series used in this post: GDP, GFDEBTN, GFDEGDQ188S, M2NS


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