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May 6, 2021
How Has GDPNow Performed during the Pandemic?
According to the U.S. Bureau of Economic Analysis (BEA), real gross domestic product (GDP) grew at an annualized rate of 6.4 percent in the first quarter of 2021, 1.5 percentage points below our final GDPNow model forecast. The size of this forecast error is large relative to GDPNow's prepandemic history but much smaller than the extremely large errors recorded last year. In short, the GDPNow model has struggled to accurately capture the unusual sources of variation in GDP caused by the COVID-19 pandemic and the various policy responses. The colored dashed lines in chart 1 show the evolution of GDPNow's forecast errors for each of the last five quarters. Unlike any of the four quarters of 2020, GDPNow's forecasts of real GDP growth last quarter was always within 5 percentage points of the BEA's first published estimate. Nevertheless, we can see that the GDPNow forecasts for GDP growth last quarter were still less accurate than before the pandemic.
Despite the recent deterioration in GDPNow's accuracy, its forecasts over the past 15 months share some broad similarities with professional forecasts, as chart 2 shows. From mid-March to mid-May 2020, both GDPNow and the consensus Wall Street Journal Economic Forecasting Survey (WSJ) forecasts of real GDP growth declined sharply. As described here, GDPNow did not anticipate the extent of the plunge in services consumption last March and the resulting impact on GDP growth for the first quarter of last year. The nosedive and rebound in economic activity last year were evident in GDPNow's forecasts for GDP growth in the second quarter of 2020. Forecasts for third-quarter GDP growth strengthened during much of the summer and early fall, and GDPNow performed as well or better than the consensus forecast over much of that period. However, for the last two quarters, GDPNow forecasts were more erratic, and generally less accurate, than the consensus.
It's not surprising that GDPNow's accuracy has deteriorated relative to the consensus of professional forecasts. Unlike GDPNow, professional forecasters have been able to use data on personal mobility and government mitigation measures (such as here, here, here, and here), and a number of studies have shown that these measures relate to cross-country differences in GDP growth observed in 2020 (for example, here and here). Transportation services and leisure and hospitality accounted for half the decline in consumer spending in the first two quarters of 2020, and a number of high-frequency measures of activity in these sectors (such as here, here, here, and here) are available before official spending data are. Finally, spikes in consumer spending following the distribution of stimulus payments included in the December and March fiscal packages were evident in some high-frequency measures of consumer spending (available here and here) before they were evident in the retail sales and personal income and outlays releases. In contrast, GDPNow uses a fixed methodology and source data for estimating GDP, making it much less adaptable to unusual circumstances.
Nevertheless, GDP forecasting during the pandemic has been challenging even for professional forecasters. Chart 3 shows the distributions of GDPNow and WSJ survey panelist forecast errors of real GDP growth for projections made about 20 days before the BEA's release of its initial GDP estimate. The gray cross marks represent individual WSJ panelist errors, and the blue rectangles represent interquartile ranges (IQRs), or middle halves, of the distribution of their forecast errors. The IQRs widened during the pandemic, and a number of panelists had errors in the middle two quarters of last year that were dramatically larger than usual.
Still, it is plausible that the extreme fluctuations in the data used by the GDPNow model have embedded some inaccuracies into the GDPNow forecasts. To examine this question, let's consider GDPNow's final projections of the main subcomponent contributions to overall real GDP growth since the model was released to the public in 2014. Chart 4 shows the stacked forecast errors of these projections. By construction, the sum of the bars in any quarter is approximately equal to the black dot showing the difference between the first official estimate of real GDP growth and that quarter's final GDPNow forecast. The bars have become much larger since the pandemic, but especially for services consumption and state and local government expenditures (S&L). In this post, I've noted issues related to services consumption, but most of the huge third-quarter S&L error fell into a category referred to as "sales to other sectors ." GDPNow expected these sales—which are largely health, hospital, tuition, and other educational charges and fees—to continue plummeting in the third quarter instead of rebounding strongly, as they did. (The S&L contribution error is negative because the BEA subtracts these sales from the S&L subcomponent, and from GDP, to avoid double-counting). The S&L contribution errors in the last two quarters were still quite large, and mostly the result of the GDPNow model incorrectly continuing to forecast declines of these "sales to other sectors."
In the coming months, we plan to tweak the architecture behind GDPNow in hope of mitigating these sorts of errors. For instance, the day before the most recent GDP release, a modified version of the GDPNow model (one that reduces the influence of the pandemic on some of its forecasting equations) predicted first-quarter real GDP growth of 5.5 percent. More than half of the difference between this forecast and the official final GDPNow forecast of 7.9 percent growth was the result of a lower forecast for growth in the S&L subcomponent (1.3 percent), a number much closer to the BEA's S&L growth estimate (1.7 percent) than the final GDPNow forecast (14.6 percent). Such enhancements might help improve GDPNow's forecasting accuracy down the line. But GDPNow will remain susceptible to forecast inaccuracies whenever unusual events (such as the COVID-19 pandemic) hit the economy and dramatically shift spending patterns.
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.
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.
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.
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.
November 6, 2017
Building a Better Model: Introducing Changes to GDPNow
Among the frequently asked questions on GDPNow's web page is this one:
Is any judgment used to adjust the forecasts? Our answer:
No. Once the GDPNow model begins forecasting GDP growth for a particular quarter, the code will not be adjusted until after the "advance" estimate. If we improve the model over time, we will roll out changes right after the "advance" estimate so that forecasts for the subsequent quarter use a fixed methodology for their entire evolution.
This macroblog post enumerates a number of minor changes to GDPNow that were implemented on October 30, when it began forecasting fourth-quarter real gross domestic product (GDP) growth. Here is a summary of the changes, intended to improve the accuracy of the GDP subcomponent forecasts:
- Services personal consumption expenditures (PCE). Use industrial production of electric and gas utilities to nowcast real PCE on electricity and natural gas. Use international trade data on travel services to forecast revisions to related PCE travel data.
- Real business equipment investment. Use/forecast data from the advance U.S. Census Bureau reports on durable manufacturing and international trade in goods that, previously, hadn't been utilized until the full reports on manufacturing and/or international trade .
- Real nonresidential structures investment. Replace a discontinued seasonally adjusted producer price index for "Steel mill products: Steel pipe and tube" with a nonseasonally adjusted version. The index is used to construct a price deflator for private monthly nonresidential construction spending.
- Real residential investment. Use employment data for production and nonsupervisory employees of residential remodelers to help forecast real investment in residential improvements.
- Real change in private inventories. Use published monthly inventory levels in the U.S. Bureau of Economic Analysis's underlying detail tables 1BU and 1BUC after the third-release GDP estimate from the prior quarter to estimate inventory levels for a number of industries in the first month of the quarter forecasted by GDPNow.
- Federal, state, and local government spending. Forecast investment in intellectual property products for these subcomponents using autoregression models.
The first three columns of the following table decompose the official estimate of the third-quarter real GDP growth rate, and forecasts of the growth rate from the discontinued and modified versions of GDPNow, into percentage point contributions from the subcomponents of GDP.
As the table shows, the methodological changes did not have much of an impact on the final third-quarter subcomponent forecasts—apart from inventory investment, where the modifications lowered the contribution to growth from 0.80 percentage points to 0.60 percentage points—or on their accuracy. Nevertheless, the topline GDP forecast of the modified model (2.3 percent) was less accurate than the previous version (2.5 percent). In the discontinued version of GDPNow, an overestimate of the inventory investment contribution to growth partly canceled out underestimated contributions from each of net exports, government spending, and nonresidential fixed investment.
In the modified version, the inventory contribution was also underestimated and did not cancel out these other errors. The last two columns of the table show that all of the subcomponent errors of the modified model were at least as small as their historical average for the discontinued version. However, the topline GDP forecast was less accurate than average because of less cancellation of the subcomponent errors than usual. We hope that the cancellation of subcomponent errors in the modified model will be more similar to the historical average in the discontinued version in the future.
Although the methodological changes could have more of an impact than the table suggests, we do not expect them to have a substantial impact in general. For example, on October 30, the discontinued version of GDPNow projected 3.0 percent GDP growth in the fourth quarter, which was little different from the modified model forecast of 2.9 percent growth. We provide a more detailed explanation of the changes to GDPNow here . Going forward, this same document will document any further changes to the model and when we made them.
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