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Policy Hub: Macroblog provides concise commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues for a broad audience.

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May 3, 2021

Is There a Global Factor in U.S. Bond Yields?

The answer to this question seems obvious simply from observing the secular comovement of global nominal yields across some advanced economies plotted in chart 1.

Chart 1: 10-Year Bond Yields, 1992-2021

This observation raises the possibility that domestic bond yields, including those in the large U.S. Treasury market, may be anchored by global economic developments (see, for example, hereOff-site link and hereOff-site link), provision of global liquidity, and international markets arbitrage. The synchronized dynamics in global yields during the last few months serve as a stark reminder of the powerful role that global bond markets play in the transmission of country-specific shocks as well as of monetary and fiscal impulses.

Yet the standard term structure models (see, for example, hereOff-site link), that policymakers and market participants use to form their expectations about the future path of the policy rate, are typically estimated only with information embedded in domestic yields. Global influences enter only via the term premia—that is, the extra returns that investors demand to hold long-term bonds—and are influenced by the flight to safety and arbitrage across international markets. But because the term premia are obtained as a residual component in the model, any misspecification of the factor structure that drives equilibrium interest rates—by omitting a common global factor, for example—may result in erroneously attributing some fundamental movements to the term premia.

Chart 2 illustrates this point, presenting a less-noticed and even overlooked empirical regularity between the term premiaOff-site link on the spread between the 10-year U.S. bond and the 10-year/2-year German bond, which is the benchmark bond for the Eurozone government bond market. This comovement has proved remarkably strong since 2014.1

Chart 2: German Bond Spread and U.S. Term Premia, 2010–21

Take, for example, the pronounced decline in the term premia and the accompanying slide in the German bond spread between 2014 and 2019. Although technical factors might be behind the downward trend in the German bond spread—for example, large Eurozone bond outflows triggered by the euro-area crisis and the introduction of negative interest rates—the slope of the yield curve could also convey important information about the fundamentals of the economy. If the term premia on the 10-year U.S. bond reflect an exogenous "distortion" in the U.S. yield curve due to a flight to safety or an elevated demand for global safe assets, yields are likely to return to normal levels when the uncertainty shock dissipates. In contrast, if investors interpret the yield curve's decline as an endogenous "risk-off" response—that is, a switch to less risky assets—to a deteriorated global environment that can spill over to the U.S. economy, the term structure model would require a "global" factor whose omission may otherwise contaminate an estimate of the term premia.

So how sensitive is the estimate of the future path of policy rate to model specification? I next illustrate this sensitivity by augmenting the factor space in a standard (five-factor) term structure model with incremental information from an additional global factor, not contained in the other factors. Given the reasonably tight correlation between the term premia for the 10-year U.S. bond and the 10-year/2-year German bond spread, it seems natural to use the latter as an observed proxy for a global factor, although other statistical approaches for extracting one or more common global factors are certainly possible.

To quantify the potential effect of the global factor, I focus on yield curve dynamics seen in 2019, a period characterized by elevated economic, trade, and geopolitical uncertainty that led to a material decline in observed yields. But did a fundamental shift in the expected path of policy rate, or lower term premia, drive this decline? In the left panel of chart 3, I plot the expected policy rate paths for the second quarter (or midpoint) of 2019, obtained from models with and without a global factor. (Recall that in the second quarter of 2019, the target range for the federal funds rate was 2.25 percent to 2.50 percent.)

Chart 3: Model-Implied Paths of Policy Rate

The difference in the shape of the expected policy rate paths implied by the two models is striking. (The models' estimates use unsmoothed yield data at quarterly frequency, with continuous bond maturities from one to 80 quarters.) Although the expected policy rate path for the standard model is fairly flat, the rate path for the model with a global factor is deeply inverted up to five-year maturities, suggesting that over this horizon one could have expected rate cuts of almost 100 basis points. These expectations occurred against the backdrop of stable growth and inflation outlook in the United States but deteriorating global economic and trade conditions. The right panel of chart 3 displays the evolution of the expected rate path, estimated from the global factor model, for the two quarters before and the two quarters after the second quarter of 2019, as the Federal Reserve started to adjust its policy rate lower. It is worth noting that the strong effect of the German 10-year/2-year spread in the term structure model with global factor is a relatively recent phenomenon. (Additional results suggest that this factor has only a muted impact on the model estimates prior to 2014.)

The policy implications of these findings warrant several remarks. One direct implication is that the common global determinants of the neutral rate of interest, as well as inflationary dynamics, could constrain the potency of domestic monetary policy. A prime example of these constraints was the policy rate normalization phase undertaken by the Fed during the 2016–18 period, which was characterized by global disinflationary pressures, underwhelming economic performance in Europe and Japan, slowing economic growth in China, and escalating trade tensions. These forces were potentially counteracting the Fed's policy efforts and exerting downward pressure on the global neutral rate of interest. The recent economic and financial developments resulting from the COVID-19 pandemic (such as the global nature of the shock, synchronized monetary and fiscal response across countries, and international financial market comovements) and the ongoing recovery appear to only strengthen the case for the importance of incorporating global information in bond-pricing models.

1 [go back] I should note that the correlation between the two series increased from 52.9 percent before 2014 to 76.2 percent after 2014. Interestingly, the beginning of 2014 marks another important shift in financial markets: a sharp and persistent compression in the breakeven inflation forward curve, as a Liberty Street Economics blog postOff-site link recently discussed. A similar flattening is present in the forward term premia of nominal bonds. This is consistent with the interpretation that such flattening—starting in 2014—is likely the result of a new regime, characterized by the compression of inflation risk across maturities.

May 18, 2020

A Couple of Insights from the April Current Population Survey

The latest reading of the Atlanta Fed’s Wage Growth Tracker indicates that wage growth is slowing. It came in at 3.3 percent for April, down from 3.5 percent in March and 3.7 percent in February. This slowing primarily reflects the relatively large decline in the employment of those who typically experience the fastest wage growth: young workers. In February, those aged 16–24 accounted for about 12 percent of employment. By April, that share had dropped to under 10 percent. This change has significant bearing on the Wage Growth Tracker because those aged 16–24 had median wage growth of around 7.8 percent on average over the last year, versus 3.6 percent for all workers. So their decreased share of employment has helped pull overall median wage growth lower (see here for more discussion).

Note that while the tracker reflects the compositional change in who is employed, it didn’t show a spike in wage growth suggested by the average hourly earnings data from the Bureau of Labor Statistics' Payroll Survey. This is because the average hourly earnings data are a snapshot of the average earnings of all workers, hence last year's average will include people who are not employed today (and vice versa). As a result, the spike in average earnings was for an awful reason: a lot of low-wage workers lost their jobs. In contrast, the tracker compares the wages of the fortunate people who were employed both today and a year earlier.

Another wage development to keep an eye on are wage freezes. During the Great Recession, there was a large and persistent increase in the fraction of workers who said their wage was unchanged from a year earlier. We will be examining the Wage Growth Tracker data for evidence of an increased incidence of wage freezes or even wage cuts. The fraction of people reporting no change in their wage has increased from 13.7 percent in February to 14.1 percent in April. In contrast, the cyclical low for this series was 12.7 percent in November of 2019.

The April data also revealed a sharp increase in the number of people who are employed but on unpaid absence from work for "other reasons." As described in this recent macroblog post, these are most likely people whose employers furloughed them. March saw an estimated 1.5 million such workers. In April, that number swelled to 6.2 million. If those people had been counted as unemployed instead of employed, the unemployment rate would have been 18.7 percent in April instead of the official number of 14.7 percent. Going forward, a gauge of the strength of the labor market recovery will be how many of these furloughed workers eventually return to work versus become unemployed—or even leave the labor force. Stay tuned.

John Robertson, a senior policy adviser in the Atlanta Fed's research department

May 15, 2020

Introducing the CFO Survey

For almost 25 years, the Duke CFO Global Business Outlook has provided policymakers, academics, and the public with an understanding of how financial executives view the economy and prospects for their business. Today, three partners—Duke University's Fuqua School of Business, the Federal Reserve Bank of Richmond, and the Federal Reserve Bank of Atlanta—announce an enhanced iteration of this survey, now called theCFO Survey. Starting with the second-quarter data release on July 8, 2020, the CFO Survey will offer the same crucial information about the economic outlook and, through some methodological updates, an enhanced look at how U.S. companies perceive and react to the current economic environment.

This partnership comes at an opportune time. As the country faces considerable economic and political uncertainty, the on-the-ground information policymakers receive from businesses has never been more important. In the longer run, the information collected through the CFO Survey will help economists and researchers understand how firms reacted amid the COVID-19 pandemic and its economic consequences. It will therefore provide a key input into our understanding of the role that sentiment and uncertainty play in corporate decision-making processes.

What will change?

Much of the Duke survey will remain the same. One change is a discontinuation of the international portion. The CFO Survey will initially survey only U.S. firms, to fully establish the U.S. panel. (After we've reached our domestic panel-composition goals, we hope to eventually engage our global partners again.) Another change is that the sampling design has moved from a repeated cross-section to a panel-data format, meaning that the same pool of business leaders will participate each quarter. Finally, the team engaged in a thorough methodological review, and the survey questions will be streamlined and the survey process made more efficient for participants. More details on changes to existing questions will be available over the course of the next few weeks, and the first set of data generated using the updated questionnaire design will be available on July 8 on the new CFO Survey website: www.cfosurvey.org.

What will stay the same?

The survey will continue to track business sentiment over time and ask questions pertaining to business leaders' most pressing concerns, their expectations for their own firms' performance, and their expectations for the performance of the U.S. economy over the year ahead. Because Duke has conducted this survey since 1996, the rich historical data will allow for contextual insights on key indicators including revenue, capital expenditures, and employment as well as illuminating trends and shifts in business sentiment. The headline CFO Optimism Index will remain unchanged—that index measures business leaders' optimism about the U.S. economy and their own firm's financial prospects.

The objectives of the survey will not change, nor will the target participants. In addition to chief financial officers, the CFO Survey panel includes treasurers, vice presidents, and directors of finance, owner-operators, accountants, controllers, and others with financial decision-making roles in their organizations. To get the broadest view possible, the CFO Survey panel includes representatives from firms that range in size from owner-operators to Fortune 500 companies and covers all major industries. Finally, the survey will remain quarterly, and aggregated survey results and analysis will be publicly available via the new CFO Survey website.

We are excited to continue to provide this valuable complement to the array of existing data available to policymakers, business decision-makers, academic researchers, and the public. For more information and for the new quarterly results, check www.cfosurvey.org on July 8, 2020. Of course, we'll also alert you here when the time comes!

John Graham, the D. Richard Mead Jr. Family Professor of Finance at Duke University's Fuqua School of Business,

Brent Meyer, a policy adviser and economist in the Atlanta Fed's Research Department,

Nicholas Parker, the Atlanta Fed's director of surveys, and

Sonya Ravindranath Waddell, a vice president and economist at the Federal Reserve Bank of Richmond


May 12, 2020

Challenges in Nowcasting GDP Growth

Real gross domestic product (GDP) declined at an annualized rate of 4.8 percent in the first quarter, according to the first estimate from the U.S. Bureau of Economic Analysis (BEA), 3.8 percentage points more than the decline anticipated by the Atlanta Fed's final GDPNow model projection. Why was the error, which was easily the model's largest on record for final GDPNow forecasts, so big? Chart 1 looks at GDPNow's forecast errors since the model went live in mid-2014 and breaks them down into forecast errors for the various subcomponents' contributions to GDP growth. 

Chart 1: Real-Time GDPNow Forecast Errors of Real GDP Growth and Component Contributions Right before Advance Release

The clear culprit is the fact that the GDPNow model did not anticipate the record 9.5 percent monthly decline (not annualized) in real services consumption in March. At that time, GDPNow had March services data available only for electricity and natural gas use and purchased meals and beverages, as well as revised February data for net foreign travel. If the model would have correctly forecasted the March growth rate in services consumption for the subcomponents besides these, it would have actually slightly overstated the first-quarter decline in real consumer spending. (I should note that because of the timing and impact of last quarter's social distancing efforts stemming from COVID-19, the BEA used data outside of the scope of its routine procedures to estimate part of services spending in March—in particular, data about private credit card transactions for health care and recreation services.)

By design, GDPNow is a purely model-based prediction method as opposed to the models of some private forecasters, who were able to incorporate developments related to COVID-19 into their April forecasts for first-quarter GDP growth in a way that GDPNow did not and could not. As a result, their GDP predictions turned out to be relatively more accurate. For example, the consensus forecast of first-quarter GDP from the Wall Street Journal Economic Forecasting Survey in the first full week of April was a decline of 3.3 percent, and the CNBC Rapid Update survey late in the week prior to the GDP release anticipated a decline in GDP of 5.3 percent. Private forecasts will continue to be able to use news developments and high-frequency or nonstandard data sources (such as initial unemployment claims and OpenTable restaurant dining data) in a way that GDPNow and similar nowcasting models do not. The New York Fed's recently introduced Weekly Economic Index combines a set of weekly indicators into a single index with units comparable to four-quarter GDP growth, but it does not actually nowcast quarterly GDP growth.

Around the time of recessions, macroeconomic projections from professional forecasters tend to be less accurate and show more dispersion than during nonrecessionary periods. And though the National Bureau of Economic Research has not identified a 2020 business cycle peak, recent GDP forecasts show much more dispersion than they did during, or close to, past recessions. Chart 2 shows the difference between the top 10 and bottom 10 average forecasts of real GDP growth (for both the current quarter and one quarter ahead) in the Blue Chip Economics Indicators survey administered in the middle month of each quarter since 1991.

Chart 2: Difference between Top 10 and Bottom 10 Average Forecasts of Quarterly Real GDP Growth in Blue Chip Economic Indicators

The top 10/bottom 10 difference for current-quarter GDP forecasts in the May 2020 survey is clearly much larger than around past recessions. In fact, it's larger than the difference between the highest and lowest quarterly growth rate of GDP after 1983, around the time economists date the onset of the Great Moderation—in reference to the decline in macroeconomic volatility—in the mid-1980s. Prior to the May 8 employment release, GDPNow was more optimistic about second-quarter GDP growth than most private forecasters were, but after the model forecast was revised down from a decline of 17.6 percent to a decline of 34.9 percent on the heels of that report, it fell more in line with the others.

The dispersion in the forecasts for GDP in the third quarter of 2020 is even starker. The optimistic forecasters project 2020:Q3 growth to be well above the highest rate on record (15.7 percent in 1950:Q1), and the pessimistic forecasters project contracting GDP. Of course, we will not be able to determine how accurate forecasts of second- and third-quarter GDP growth are until later in the year. Nevertheless, the wide range of forecasts implies that at least some forecasters' GDP projections will be wildly off by historical standards. As St. Louis Fed economist Michael McCracken recently noted, what the late Yankees catcher Yogi Berra said is more true than ever: "It's tough to make predictions, especially about the future." Berra's wisdom also will also apply to producing accurate and reliable economic forecasts for some time to come.