Federal Reserve Economic Data

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Measuring and comparing local economies: Memphis vs. Nashville

When we want to assess the national economy, we typically look at the growth rate of gross domestic product (GDP), as it accounts for all goods and services produced in the economy. Similar data are also available for local economies in a measure called gross metropolitan product (GMP).

Unfortunately, local GMP data are calculated only once per year and released with a nine-month lag. For more timely information, we use factor analysis to estimate the common trend (or factor) underlying the movement in 12 variables of regional economic activity. This common factor is used to produce an index of economic activity for 51 MSAs across the nation. Each index is calibrated to match the annual growth rate and volatility of GMP for the MSA. In other words, the value of the index can be interpreted as an annual growth rate of the local economy, which allows for ease of interpretation and comparison across metro areas. For more details, see this working paper.

Just as job growth and unemployment rates vary from one region to the next, economic growth of two areas (even in the same state) can also vary. The graph plots the economic conditions indexes of the two largest metro areas in Tennessee, and we can see how the Nashville metro area has grown at a faster pace than the Memphis metro area over the past few decades. During the 2001 recession, Nashville’s economy actually expanded most months. During the Great Recession, growth was negative in both regions, with growth resuming in Nashville in August 2009 and in Memphis in March 2010. In recent years, the Nashville economy has grown steadily at around 5 percent, while the Memphis economy has slowed from about 2.5 percent per year to around 1.5 percent.

How this graph was created:
Search for “metro area economic conditions” and select the metro areas you want to add to the graph. Or go to the Economic Conditions Index Release table, select the MSAs you want to view, and select “Add to Graph” at the bottom of the table.

Suggested by Maria Arias and Charles Gascon

View on FRED, series used in this post: MPHAGRIDX, NVLAGRIDX

Illiquidity in the bond market

An asset is said to be “liquid” if traders can convert it quickly to cash without materially affecting its market price. The market for large cap stocks is liquid because equity claims are relatively homogeneous and there are normally large numbers of buyers and sellers trading on centralized exchanges. Most bond markets are highly illiquid, primarily because bonds are highly idiosyncratic. Even bonds issued by the same entity normally differ along several dimensions, including maturity, coupon rate, and covenants. Because this is so, bonds typically trade over-the-counter (OTC)—that is, in a decentralized trading environment where idiosyncratic bonds must be matched with willing buyers. These markets are typically very thin, and most bonds do not even trade on secondary markets. The bonds issued by sovereigns and large corporations are an exception. But even these bonds trade largely in decentralized OTC markets.

There has been a growing concern as of late that liquidity conditions in even relatively liquid bond markets have deteriorated in recent years. If this is so, then even modest events may trigger an unexpected and undesirable disruption in financial markets. In the summer of 2013, for example, when Fed chair Ben Bernanke hinted at a possible slowdown in the pace of Fed bond purchases, the bond market reacted violently in what was described as a “taper tantrum.” Another example is the Oct. 15, 2014, “flash rally” in which the 10-year on-the-run U.S. Treasury experienced an incredible 40 basis point movement in a single day for no apparent reason. According to a report released by the U.S. Treasury Department, it seems that for a brief period of time there were far more trades to buy Treasuries than trades to sell. That this happened in the most liquid of all bond markets raises a concern with other less-liquid bond markets. Might a modest increase in the Fed policy rate induce a “rush to the exits,” forcing a fire sale of bonds into an illiquid market to meet redemption payments?

It is, in fact, very difficult to know whether liquidity conditions are deteriorating in bond markets. Standard measures, such as bid-ask spreads, are of little help because historically narrow bid-ask spreads can widen suddenly in a liquidity event. Some commentators have pointed to the post-financial-crisis behavior of the 22 primary dealers, who play an important role as market makers for bonds. In fact, primary dealer inventories in corporate bonds have declined from over $250 billion in 2007 to about $50 billion in 2015. Since 2007, the supply of U.S. corporate bonds has increased from about $3.2 trillion to almost $5.0 trillion (see graph above), so that the dealer inventory relative to outstanding debt has dropped precipitously. Moreover, prior to 2007, dealers were net long in corporate bonds and net short in U.S. Treasuries. Dealers are now net long in Treasuries. This, together with their reduced holdings of corporate securities, suggests that dealers’ willingness and/or ability to take on risk has diminished greatly since 2008. Many commentators blame the Volcker rule, which was designed to curtail the proprietary trading activities of dealer banks.

Whether these behaviors contribute to reduce bond market liquidity is difficult to judge. In fact, one could make the case that the dealer banks are in much better position than they were in 2007 to absorb a liquidity event, for example, by absorbing a sell-off in corporate bonds with sales of Treasuries. Much of the bond supply is intermediated through money market mutual funds. Historically, these funds have sought to maintain fixed exchange rate regimes subject to speculative attack. The vulnerability of these funds to mass redemption events, however, may be curtailed with the passing of Rule 2a-7 by the U.S. Securities and Exchange Commission. This new rule requires that funds adopt a floating exchange rate regime (floating net asset value) and permits the imposition of liquidity fees and redemption gates at the discretion of the funds’ board of directors. These rules are consistent with the ones prescribed in Diamond and Dybvig (1983) for the prevention of bank runs.

It is important to understand, however, that such measures are not a guarantee against price volatility. They are simply measures to mitigate the “excess” price volatility that accompanies thinly traded markets. If everyone wants to sell bonds, their price will decline even in the most liquid of bond markets.

How this graph was created: Search for the (quarterly) series shown above and add it to the graph. Restrict the sample period to start in 2001.

Suggested by David Andolfatto

View on FRED, series used in this post: NCBDBIQ027S

A good use of moving averages

Some data series are very volatile. That is, they don’t follow a smooth or step-by-step pattern. And it’s difficult to draw conclusions when new data are added to a volatile series. The weekly release of initial claims for unemployment insurance is a great example. In this and similar cases, it is useful to adopt some kind of smoothing mechanism: Here we provide a four-week moving average. Traditionally, a moving average is centered—say, the average of two periods before and two periods after. This moving average takes the last four observations, which allows you to better read trends, especially if you’re focusing on the most recent data. Of course, trends become more obvious if you look at longer spans of time. This graph shows a span of five years. Narrow or expand the sample with the slide bar to see how a moving average can help you interpret the data and avoid the pitfalls of volatility.

How this graph was created: Search for “initial claims,” select the two (seasonally adjusted) series, and add them to the graph. Finally, restrict the sample to the last 5 years, which is done by using the settings above the graph on the right.

Suggested by Christian Zimmermann

View on FRED, series used in this post: IC4WSA, ICSA


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