The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.
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February 13, 2018
GDPNow's Forecast: Why Did It Spike Recently?
If you felt whipsawed by GDPNow recently, it's understandable. On February 1, the Atlanta Fed's GDPNow model estimate of first-quarter real gross domestic product (GDP) growth surged from 4.2 percent to 5.4 percent (annualized rates) after a manufacturing report from the Institute for Supply Management. GDPNow's estimate then fell to 4.0 percent on February 2 after the employment report from the U.S. Bureau of Labor Statistics. GDPNow displayed a similar undulating pattern early in the forecast cycle for fourth-quarter GDP growth.
What accounted for these sawtooth patterns? The answer lies in the treatment of the ISM manufacturing release. To forecast the yet-to-be released monthly GDP source data apart from inventories, GDPNow uses an indicator of growth in economic activity from a statistical model called a dynamic factor model. The factor is estimated from 127 monthly macroeconomic indicators, many of which are used to estimate the Chicago Fed National Activity Index (CFNAI). Indices like these can be helpful for forecasting macroeconomic data, as demonstrated here and here.
Perhaps not surprisingly, the CFNAI and the GDPNow factor are highly correlated, as the red and blue lines in the chart below indicate. Both indices, which are normalized to have an average of 0 and a standard deviation of 1, are usually lower in recessions than expansions.
A major difference in the indices is how yet-to-be-released values are handled for months in the recent past that have reported values for some, but not all, of the source data. For example, on February 2, January 2018 values had been released for data from the ISM manufacturing and employment reports but not from the industrial production or retail sales reports. The CFNAI is released around the end of each month when about two-thirds of the 85 indicators used to construct it have reported values for the previous month. For the remaining indicators, the Chicago Fed fills in statistical model forecasts for unreported values. In contrast, the GDPNow factor is updated continuously and extended a month after each ISM manufacturing release. On the dates of the ISM releases, around 17 of the 127 indicators GDPNow uses have reported values for the previous month, with six coming from the ISM manufacturing report.
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For months with partially missing data, GDPNow updates its factor with an approach similar to the one used in a 2008 paper by economists Domenico Giannone, Lucrezia Reichlin and David Small. That paper describes a dynamic factor model used to nowcast GDP growth similar to the one that generates the New York Fed's staff nowcast of GDP growth. In the Atlanta Fed's GDPNow factor model, the last month of ISM manufacturing data have large weights when calculating the terminal factor value right after the ISM report. These ISM weights decrease significantly after the employment report, when about 50 of the indicators have reported values for the last month of data.
In the above figure, we see that the January 2018 GDPNow factor reading was 1.37 after the February 1 ISM release, the strongest reading since 1994 and well above either its forecasted value of 0.42 prior to the ISM release or its estimated value of 0.43 after the February 2 employment release. The aforementioned rise and decline in the GDPNow forecast of first-quarter growth is largely a function of the rise and decline in the January 2018 estimates of the dynamic factor.
Although the January 2018 reading of 59.2 for the composite ISM purchasing managers index (PMI) was higher than any reading from 2005 to 2016, it was little different than either a consensus forecast from professional economists (58.8) or the forecast from a simple model (58.9) that uses the strong reading in December 2017 (59.3). Moreover, it was well above the reading the GDPNow dynamic factor model was expecting (54.5).
A possible shortcoming of the GDPNow factor model is that it does not account for the previous month's forecast errors when forecasting the 127 indicators. For example, the predicted composite ISM PMI reading of 54.4 in December 2017 was nearly 5 points lower than the actual value. For this discussion, let's adjust GDPNow's factor model to account for these forecast errors and consider a forecast evaluation period with revised current vintage data after 1999. Then, the average absolute error of the 85–90 day-ahead adjusted model forecasts of GDP growth after ISM manufacturing releases (1.40 percentage points) is lower than the average absolute forecast error on those same dates for the standard version of GDPNow (1.49 percentage points). Moreover, the forecasts using the adjusted factor model are significantly more accurate than the GDPNow forecasts, according to a standard statistical test . If we decide to incorporate adjustments to GDPNow's factor model, we will do so at an initial forecast of quarterly GDP growth and note the change here .
Would the adjustment have made a big difference in the initial first-quarter GDP forecast? The February 1 GDP growth forecast of GDPNow with the adjusted factor model was "only" 4.7 percent. Its current (February 9) forecast of first-quarter GDP growth was the same as the standard version of GDPNow: 4.0 percent. These estimates are still much higher than both the recent trend in GDP growth and the median forecast of 3.0 percent from the Philadelphia Fed's Survey of Professional Forecasters (SPF).
Most of the difference between the GDPNow and SPF forecasts of GDP growth is the result of inventories. GDPNow anticipates inventories will contribute 1.2 percentage points to first-quarter growth, and the median SPF projection implies an inventory contribution of only 0.4 percentage points. It's not unusual to see some disagreement between these inventory forecasts and it wouldn't be surprising if one—or both—of them turn out to be off the mark.
January 18, 2018
How Low Is the Unemployment Rate, Really?
In 2017, the unemployment rate averaged 4.4 percent. That's quite low on a historical basis. In fact, it's the lowest level since 2000, when unemployment averaged 4.0 percent. But does that mean that the labor market is only 0.4 percentage points away from being as strong as it was in 2000? Probably not. Let's talk about why.
As observed by economist George Perry in 1970, although movement in the aggregate unemployment rate is mostly the result of changes in unemployment rates within demographic groups, demographic shifts can also change the overall unemployment rate even if unemployment within demographic groups has not changed. Adjusting for demographic changes makes for a better apples-to-apples comparison of unemployment today with past rates.
Three large demographic shifts underway since the early 2000s are the rise in the average age and educational attainment of the labor force, and the decline in the share who are white and non-Hispanic. These changes are potentially important because older workers and those with more education have lower rates of unemployment across age and education groups respectively, and white non-Hispanics tend to have lower rates of unemployment than other ethnicities.
The following chart shows the results of a demographic adjustment that jointly controls for year-to-year changes in two sex, three education, four race/ethnicity, and six age labor force groups, (see here for more details). Relative to the year 2000, the unemployment rate in 2017 is about 0.6 percentage points lower than it would have been otherwise simply because the demographic composition of the labor force has changed (depicted by the blue line in the chart).
In other words, even though the 2017 unemployment rate is only 0.4 percentage points higher than in 2000, the demographically adjusted unemployment rate (the green line in the chart) is 1.0 percentage points higher. In terms of unemployment, after adjusting for changes in the composition of the labor force, we are not as close to the 2000 level as you might have thought.
The demographic discrepancy is even larger for the broader U6 measure of unemployment, which includes marginally attached and involuntarily part-time workers. The 2017 demographically adjusted U6 rate is 2.5 percentage points higher than in 2000, whereas the unadjusted U6 rate is only 1.5 percentage points higher. That is, on a demographically adjusted basis, the economy had an even larger share of marginally attached and involuntarily part-time workers in 2017 than in 2000.
The point here is that when comparing unemployment rates over long periods, it's advisable to use a measure that is reasonably insulated from demographic changes. However, you should also keep in mind that demographics are only one of several factors that can cause fluctuation. Changes in labor market and social policies, the mix of industries, as well as changes in the technology of how people find work can also result in changes to how labor markets function. This is one reason why estimates of the so-called natural rate of unemployment are quite uncertain and subject to revision. For example, participants at the December 2012 Federal Open Market Committee meeting had estimates for the unemployment rate that would prevail over the longer run ranging from 5.2 to 6.0 percent. At the December 2017 meeting, the range of estimates was almost a whole percentage point lower at 4.3 to 5.0 percent.
January 17, 2018
What Businesses Said about Tax Reform
Many folks are wondering what impact the Tax Cuts and Jobs Act—which was introduced in the House on November 2, 2017, and signed into law a few days before Christmas—will have on the U.S. economy. Well, in a recent speech, Atlanta Fed president Raphael Bostic had this to say: "I'm marking in a positive, but modest, boost to my near-term GDP [gross domestic product] growth profile for the coming year."
Why the measured approach? That might be our fault. As part of President Bostic's research team, we've been curious about the potential impact of this legislation for a while now, especially on how firms were responding to expected policy changes. Back in November 2016 (the week of the election, actually), we started asking firms in our Sixth District Business Inflation Expectations (BIE) survey how optimistic they were (on a 0–100 scale) about the prospects for the U.S. economy and their own firm's financial prospects. We've repeated this special question in three subsequent surveys. For a cleaner, apples-to-apples approach, the charts below show only the results for firms that responded in each survey (though the overall picture is very similar).
As the charts show, firms have become more optimistic about the prospects for the U.S. economy since November 2016, but not since February 2017, and we didn't detect much of a difference in December 2017, after the details of the tax plan became clearer. But optimism is a vague concept and may not necessarily translate into actions that firms could take that would boost overall GDP—namely, increasing capital investment and hiring.
In November, we had two surveys in the field—our BIE survey (undertaken at the beginning of the month) and a national survey conducted jointly by the Atlanta Fed, Nick Bloom of Stanford University, and Steven Davis of the University of Chicago. (That survey was in the field November 13–24.) In both of these surveys, we asked firms how the pending legislation would affect their capital expenditure plans for 2018. In the BIE survey, we also asked how tax reform would affect hiring plans.
The upshot? The typical firm isn't planning on a whole lot of additional capital spending or hiring.
In our national survey, roughly two-thirds of respondents indicated that the tax reform hasn't enticed them into changing their investment plans for 2018, as the following chart shows.
The chart below also makes apparent that small firms (fewer than 100 employees) are more likely to significantly ramp up capital investment in 2018 than midsize and larger firms.
For our regional BIE survey, the capital investment results were similar (you can see them here). And as for hiring, the typical firm doesn't appear to be changing its plans. Interestingly, here too, smaller firms were more likely to say they'd ramp up hiring. Among larger firms (more than 100 employees), nearly 70 percent indicated that they'd leave their hiring plans unchanged.
One interpretation of these survey results is that the potential for a sharp acceleration in GDP growth is limited. And that's also how President Bostic described things in his January 8 speech: "For now, I am treating a more substantial breakout of tax-reform-related growth as an upside risk to my outlook."
January 04, 2018
Financial Regulation: Fit for New Technologies?
In a recent interview, the computer scientist Andrew Ng said, "Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI [artificial intelligence] will transform in the next several years." Whether AI effects such widespread change so soon remains to be seen, but the financial services industry is clearly in the early stages of being transformed—with implications not only for market participants but also for financial supervision.
Some of the implications of this transformation were discussed in a panel at a recent workshop titled "Financial Regulation: Fit for the Future?" The event was hosted by the Atlanta Fed and cosponsored by the Center for the Economic Analysis of Risk at Georgia State University (you can see more on the workshop here and here). The presentations included an overview of some of AI's implications for financial supervision and regulation, a discussion of some AI-related issues from a supervisory perspective, and some discussion of the application of AI to loan evaluation.
As a part of the panel titled "Financial Regulation: Fit for New Technologies?," I gave a presentation based on a paper I wrote that explains AI and discusses some of its implications for bank supervision and regulation. In the paper, I point out that AI is capable of very good pattern recognition—one of its major strengths. The ability to recognize patterns has a variety of applications including credit risk measurement, fraud detection, investment decisions and order execution, and regulatory compliance.
Conversely, I observed that machine learning (ML), the more popular part of AI, has some important weaknesses. In particular, ML can be considered a form of statistics and thus suffers from the same limitations as statistics. For example, ML can provide information only about phenomena already present in the data. Another limitation is that although machine learning can identify correlations in the data, it cannot prove the existence of causality.
This combination of strengths and weaknesses implies that ML might provide new insights about the working of the financial system to supervisors, who can use other information to evaluate these insights. However, ML's inability to attribute causality suggests that machine learning cannot be naively applied to the writing of binding regulations.
John O'Keefe from the Federal Deposit Insurance Corporation (FDIC) focused on some particular challenges and opportunities raised by AI for banking supervision. Among the challenges O'Keefe discussed is how supervisors should give guidance on and evaluate the application of ML models by banks, given the speed of developments in this area.
On the other hand, O'Keefe observed that ML could assist supervisors in performing certain tasks, such as off-site identification of insider abuse and bank fraud, a topic he explores in a paper with Chiwon Yom, also at the FDIC. The paper explores two ML techniques: neural networks and Benford's Digit Analysis. The premise underlying Benford's Digit Analysis is that the digits resulting from a nonrandom number selection may differ significantly from expected frequency distributions. Thus, if a bank is committing fraud, the accounting numbers it reports may deviate significantly from what would otherwise be expected. Their preliminary analysis found that Benford's Digit Analysis could help bank supervisors identify fraudulent banks.
Financial firms have been increasingly employing ML in their business areas, including consumer lending, according to the third participant in the panel, Julapa Jagtiani from the Philadelphia Fed. One consequence of this use of ML is that it has allowed both traditional banks and nonbank fintech firms to become important providers of loans to both consumers and small businesses in markets in which they do not have a physical presence.
Potentially, ML also more effectively measures a borrower's credit risk than a consumer credit rating (such as a FICO score) alone allows. In a paper with Catharine Lemieux from the Chicago Fed, Jagtiani explores the credit ratings produced by the Lending Club, an online lender that that has become the largest lender for personal unsecured installment loans in the United States. They find that the correlation between FICO scores and Lending Club rating grades has steadily declined from around 80 percent in 2007 to a little over 35 percent in 2015.
It appears that the Lending Club is increasingly taking advantage of alternative data sources and ML algorithms to evaluate credit risk. As a result, the Lending Club can more accurately price a loan's risk than a simple FICO score-based model would allow. Taken together, the presenters made clear that AI is likely to also transform many aspects of the financial sector.
- GDPNow's Forecast: Why Did It Spike Recently?
- How Low Is the Unemployment Rate, Really?
- What Businesses Said about Tax Reform
- Financial Regulation: Fit for New Technologies?
- Is Macroprudential Supervision Ready for the Future?
- Labor Supply Constraints and Health Problems in Rural America
- Building a Better Model: Introducing Changes to GDPNow
- How Ill a Wind? Hurricanes' Impacts on Employment and Earnings
- When Health Insurance and Its Financial Cushion Disappear
- What Is the "Right" Policy Rate?
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