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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

Regulatory capital

The United States and several other countries are members of the Basel Committee, a global initiative to help regulate banks and address regulators’ concerns about the way banks hold capital. In fact, the committee has published specific standards for capital requirements for member countries to implement.

Traditionally, banks have reduced the capital they’re required to hold by taking advantage of the lack of risk restrictions assigned to government bonds. But countries such as Greece have shown that government bonds do have some risk attached to them, so regulators hope to change this practice.

We can track relevant changes by looking at data in FRED: specifically, the IMF’s calculation of the ratios of regulatory capital to risk-weighted assets for several countries, which are part of their set of financial soundness indicators.

The data show similar trends in the United States and euro area: The recent recession, which underscored the risk associated with billions of dollars of bank assets, caused a large spike in the amount of capital that banks were holding. However, this amount has declined sharply in the recovery period. Interestingly, the ratios of regulatory capital actually seemed to decrease during the 2001 recession—which points to differences in the nature of the two recessions.

How this graph was created: Search for “Regulatory Capital to Risk-Weighted Assets for United States” and change the units to “Percent, Change, Percent.” Click on “add data series” and add “Regulatory Capital to Risk-Weighted Assets for Euro Area.”

Suggested by Abhinav Chhabra

View on FRED, series used in this post: DDSI05EZA156NWDB, DDSI05USA156NWDB

Economic policy uncertainty

How clear is the public’s understanding of economic policy and its likely outcomes? FRED includes a data series that seeks to answer this question: In a recent paper, Scott Baker, Nicholas Bloom, and Steven Davis developed an index that estimates the level of uncertainty about economic policy by accounting for newspaper references to uncertainty, tax codes, and disagreement among forecasters.

The authors refer to spikes in the index that occurred during important events such as “tight presidential elections, Gulf Wars I and II, the 9/11 attack, and other major shocks.” More-recent events include the Lehman Brothers bankruptcy, the euro crisis, and the debt-ceiling deadlock.

The Federal Reserve has been making an effort to reduce uncertainty by increasing clarity and transparency with respect to its policies. As noted in a previous blog post, FRED also allows you to track the projections made by FOMC members. (Read full-text FOMC statements back to 2009 on the Board’s website.)

How this graph was created: Search for “Economic Policy Uncertainty Index” and change the frequency to “Weekly, Ending Friday.”

Suggested by Abhinav Chhabra

View on FRED, series used in this post: USEPUINDXD

Economic volatility in emerging economies

By definition, “emerging” economies are in transition from “developing” status to “developed” status, which is generally a bumpy road that may involve more-frequent crises and stronger economic fluctuations. One way to visualize this transition in FRED is to graph per capita GDP growth rates for the BRIC countries (Brazil, Russia, India, China) and compare them with the rate for the U.S. (thick black line). While it is usually better to show fewer series on one graph, the idea here is simply to illustrate that these economies bounce around much more than the U.S. economy does. The U.S. rate is never the highest. On the rare occasions when it is the lowest, it is only barely so—with 2007 being the exception.

How this graph was created: Search for “Constant GDP per capita” for the various countries and add the 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: NYGDPPCAPKDBRA, NYGDPPCAPKDCHN, NYGDPPCAPKDIND, NYGDPPCAPKDRUS, NYGDPPCAPKDUSA


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