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.
November 06, 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.
May 22, 2017
GDPNow's Second Quarter Forecast: Is It Too High?
Real gross domestic product (GDP) growth slowed from a 2 percent pace in 2016 to an annual rate of 0.7 percent in the first quarter of 2017. The Federal Open Market Committee viewed this slowdown in growth "as likely to be transitory," according to its last statement.
Indeed, current quarter GDP forecasting models maintained by the Federal Reserve Banks of New York, St. Louis, and Atlanta have been pointing toward stronger second quarter growth (2.3 percent, 2.6 percent and 4.1 percent, as reported on their respective websites on May 19, 2017).
The Atlanta Fed's model—GDPNow—is at the high end of this range and is also high relative to other professional forecasts. The median forecast for second quarter real GDP growth in the May Survey of Professional Forecasters (SPF) was 3.1 percent, for instance, and recent forecasts from Blue Chip Publication surveys displayed on our GDPNow page show some divergence from our model as well.
We encourage—and frequently receive—feedback on our GDPNow tool, and some users have suggested that our forecast for second quarter growth is too high. In fact, some empirical evidence supports that view. The evidence considered here correlates differences between consensus Blue Chip Economic Indicators Survey and GDPNow forecasts for growth about 80 days before the first GDP release with the GDPNow forecast errors (see the chart below).
A note about the chart: The horizontal axis shows the difference between the Blue Chip consensus forecasts and GDPNow's forecast. The vertical axis measures the 80-day-ahead GDPNow forecast error, defined as the difference between the first published estimate of real GDP growth and the GDPNow forecast at the time of the mid-quarter Blue Chip survey.
As the chart shows, there is a positive relationship between the Blue Chip-GDPNow discrepancy and the GDPNow forecast error. A simple linear regression would predict that the GDPNow forecast of 3.7 percent growth on May 5 was too high by nearly 1.0 percentage point. Moreover, the chart suggests that there has been a bias in GDPNow forecasts since the fourth quarter of 2015 of between 0.9 and 2.0 percentage points at the time of these mid-quarter Blue Chip surveys. If you are inclined to think the GDPNow forecast for second quarter growth is a bit too high, then this evidence will not change your mind.
Given this evidence, you might think that putting relatively little stock in the GDPNow forecast at this point in the quarter would be prudent. Indeed, if we calculate the weighted average of the historical Blue Chip consensus and GDPNow forecasts that produced the most accurate forecast of the first estimate of real GDP growth, then the optimal weight of the GDPNow forecast lies somewhere between 0.34 and 0.55 (see the chart below). The weight depends on the number of days until the first GDP release.
For example, the optimal weight of 0.55 on GDPNow about 54 days before the first GDP release means that 0.55 times the GDPNow forecast plus 0.45 times Blue Chip consensus survey forecast has been more accurate, on average, than any other weighted average of the two forecasts. The lowest weight on GDPNow corresponds to forecasts made about 83 days before the first GDP release—the time when GDPNow's bean-counting algorithms have the least amount of source data to work with.
A weighted average of the Blue Chip consensus and GDPNow forecasts at that time would put the GDP forecast about 0.6 to 0.7 percentage points below the current GDPNow forecast. However, the confidence bands around these estimates are wide, so the positive weight placed on GDPNow early in the quarter could just be the result of chance.
Let's cut to the chase—why, exactly, is the GDPNow forecast for second quarter GDP growth so high? The details of the GDPNow forecast provide some clues. We can compare the GDPNow forecasts of GDP components with those from the SPF. (The Blue Chip forecast does not provide detail on all the GDP components.) The following table translates the median SPF forecasts into contributions to second quarter real GDP growth. These contributions are shown alongside GDPNow's forecasted contributions as well as the average contributions to real GDP growth over the prior four quarters.
Clearly, more than half of the difference between the GDP growth forecasts from GDPNow and the SPF is due to inventories. For both forecasts, inventory investment also accounts for over half of the pickup in second quarter growth from the trailing four-quarter average.
A macroblog post I wrote last year showed that the growth-forecast contribution of mid-quarter inventory investment produced roughly equivalent accuracy in the SPF and GDPNow models, but it was much less accurate than the contribution forecasts of the other GDP components. Based on experience, we can't be confident that either forecast of inventory investment is likely to be very accurate or that one is likely to be much more accurate than another.
With very little hard data in hand for the second quarter for most of the GDP components—and for inventories in particular—we will continue to closely monitor if the data are as strong as GDPNow is anticipating or if they hew more closely to other forecasts. Check back with us to see.
February 07, 2017
Net Exports Continue to Bedevil GDPNow
Real gross domestic product (GDP) grew at an annualized rate of 1.9 percent in the fourth quarter, according to the advance estimate from the U.S. Bureau of Economic Analysis (BEA), 1.0 percentage point below the Atlanta Fed's final GDPNow model projection. This was a sizable miss relative to other forecasts. Both the consensus estimate from the January Wall Street Journal Economic Forecasting Survey and the January 20 staff nowcast from the New York Fed were expecting 2.1 percent growth last quarter.
The miss was also large relative to the historical accuracy of the GDPNow model. As the table below shows, almost all of GDPNow's error for fourth quarter growth was concentrated in real net exports. For the other broad subcomponents, GDPNow was more accurate than usual, as the last two columns of the table show. But net exports subtracted 1.70 percentage points from real GDP growth last quarter, whereas GDPNow forecasted they would only reduce growth by 0.64 percentage points. All but 0.02 percentage points of this error was in the "goods" category as opposed to services.
Three months ago, I wrote a macroblog post showing that nearly all of GDPNow's 0.8 percentage point error for third-quarter growth was concentrated in goods net exports. That analysis explained how GDPNow's goods net exports forecast is a weighted average of two forecasts. One of these forecasts is a "bean counting" model that uses monthly source data on nominal values and price deflators for goods imports and exports. The other is a quarterly econometric model that uses subcomponents of real GDP for prior quarters. In the GDPNow model, the "bean counting" model gets nearly 60 percent of the weight just before the advance GDP release.
To see how this approach matters for the GDP forecast, the following chart shows the "real-time" forecasts of the contribution of goods net exports to growth just before BEA's advance GDP estimate from the two models alongside the advance estimate of the contribution and the final GDPNow forecast.
We see that the "bean counting" forecast has been much more accurate than the quarterly econometric forecast, particularly for the last two quarters of 2016. Not surprisingly given its name, the "bean counting" model was able to largely capture the 0.75 percentage points that soybean exports contributed to third-quarter real GDP growth and the just over 0.5 percentage points they likely subtracted from fourth-quarter growth. The econometric model was not.
The final forecasts of goods net exports from the "bean counting" model have also been more accurate than GDPNow since forecasts were first posted online in mid-2014. Does this imply that an alternative "bean counting" version of GDPNow would be preferable? The answer is less obvious than you might think. Not putting any weight on the quarterly econometric model for any GDP subcomponents yields an average error for GDP growth (without regard to sign) of 0.635 percentage points, and the same statistic for GDPNow is 0.589 percentage points. This is despite the fact that the "bean counting" approach has been more accurate than GDPNow in its forecasts of net exports and about as accurate, on balance, for the other GDP subcomponents.
The final forecast of real GDP growth last quarter of this alternative "bean counting" model was 2.8 percent—only slightly more accurate than GDPNow. (For each GDP subcomponent, I include the "bean counting" and quarterly econometric model forecasts in this excel spreadsheet.)
However, if variants like the aforementioned "bean counting" approach continue to outperform the GDPNow model in one or more dimensions, we may consider regularly reporting their forecasts along with the GDPNow forecast.
December 05, 2016
Using Judgment in Forecasting: Does It Matter?
Many professional forecasters use statistical models when making their near-term projections for real gross domestic product (GDP) growth. A 2013 special survey on the forecasting methods of the Survey of Professional Forecasters found that 18 out of 21 respondents featured a statistical model prominently in their current-quarter economic projections. Nevertheless, there is fairly compelling evidence that many professional forecasters incorporate judgment in their forecasts of the first estimate of real GDP growth for a quarter—even when much of the source data used to construct the GDP estimate are available.
In the October 2016 Wall Street Journal Economic Forecasting Survey (WSJ), the most common panelist projection for annualized third-quarter real GDP growth was 2.5 percent, and the second most common one was 3.0 percent. The first digit after the decimal point, or tenths digit, of these two numbers are "5" and "0." Of the 58 individual forecasts of third-quarter growth in the survey, 21 had a tenths digit of "0" or "5," a total that is almost twice as large as we would expect if all tenths digits were equally likely to be submitted.
This pattern isn't unique to the most recent quarter's GDP forecast. The following chart shows the historical frequency of the tenths digit in past WSJ surveys for first estimates of real GDP growth over the period from the first quarter of 2003 to the third quarter of 2016, made about three weeks before the release.
Almost 40 percent of these 2,390 forecasts have a tenths digit of "0" or "5." In contrast, the historical distribution of published first estimates of real GDP growth from the fourth quarter of 1991 to the third quarter of 2016 and real gross national product (the most common measure of U.S. production in an earlier era) growth from the third quarter of 1965 to the third quarter of 1991 has a tenths digit of either "0" or "5" only 18 percent of the time. The historical Atlanta Fed's GDPNow forecasts have a "0" or a "5" tenths digit only 15 percent of the time.
More formally, one easily can reject the hypothesis at the 1 percent significance level that the tenths digit of the WSJ panelist forecasts are either uniformly distributed or follow the Benford distribution for tenths digits after rounding to the nearest tenth (see this paper by economists Stefan Gunnel and Karl-Heinz Todter, who found similar relative frequencies of "0s" and "5s" in professional forecasts of German GDP growth and consumer price index inflation).
If we assume that near-term GDP growth forecasts with a tenths digit of "0" or "5" typically involve more judgment than forecasts with another tenths digit, a natural question is whether these more judgmental forecasts are less accurate than others. Of the 2,390 WSJ growth forecasts mentioned above, the ones with a tenths digit of "0" or "5" (after rounding to the nearest tenth) had an average error of 0.786 percentage points without regard to sign, and the others had an average error of 0.743 percentage points. These accuracy metrics are not statistically different at even the 10 percent significance level. Moreover, because of the panel nature of WSJ forecasts, we can measure how often a forecaster has a tenths digit of "0" or "5" (after rounding). Of the 44 panelists who submitted at least 30 three-week-ahead GDP forecasts during the period of the first quarter of 2003 through the third quarter of 2016, the correlation of the panelists "0" or "5" tenth digit frequency and their average error without regard to sign is only 0.13 and not significantly different from 0.
Although at least some professional forecasters appear to make judgmental adjustments to their near-term GDP projections, the evidence presented here does not suggest it comes, on average, at the cost of accuracy.
November 07, 2016
The Price Isn't Right: On GDPNow's Third Quarter Miss
The U.S. Bureau of Economic Analysis's (BEA) first estimate of third quarter annualized real gross domestic product (GDP) growth released on October 28 was 2.9 percent. A number of nowcasts were quite close to this number, including the median forecast of 3.0 percent from the CNBC Rapid Update survey of roughly 10 economists. The Atlanta Fed's GDPNow model forecast of 2.1 percent? Not so close.
What accounted for GDPNow's miss? The table below shows the GDPNow forecasts and BEA estimates of the percentage point contributions to third quarter growth of six subcomponents that together make up real GDP. The largest forecast error, both in absolute terms and relative to the historical accuracy of the projections, was for the contribution of real net exports to growth. The published contribution of 0.83 percentage points was much higher than the model's estimate of 0.07 percentage points.
Why did GDPNow miss so badly on net exports? For both goods and services real net exports, the GDPNow forecast is a weighted average of two forecasts. The "bean counting" forecast uses the monthly source data on the nominal values and price deflators of exports and imports. The econometric model forecast uses published values of 13 subcomponents of real GDP for the last five quarters to predict real net exports for both goods and services. The statistically determined weights on the bean counting forecast increase as we get closer to the first GDP release and accumulate more monthly source data. (More details are provided here.)
For real net exports of services, 89 percent of the weight was given to the bean counting forecast. This weighting worked out well last quarter as the forecasts of the contribution of real services net exports to third quarter growth from both the bean counting and combined models were within 0.01 percentage points of the published value of −0.14 percentage points. But for real net exports of goods, the bean counting forecast received only 59 percent of the weight in the final GDPNow forecast. It projected that real net exports of goods would add 0.76 percentage points to growth—reasonably close to the BEA estimate. In contrast, the econometric model projected a subtraction of 0.58 percentage points from growth.
Since GDPNow had monthly price and nominal spending data through September on goods imports and exports, why didn't it place more weight on the source data? One of the important reasons is that it's difficult to match the quarterly inflation rate of the BEA's import price deflator for goods. The BEA constructs its price deflator with detailed price indices from the Bureau of Labor Statistics (BLS) producer price index and import/export price index programs as well as a few other sources. GDPNow uses the BLS's import price data at higher levels of aggregation than the BEA uses and also differs in the manner that it handles seasonality. The chart below plots the difference between the BEA's quarterly goods import price inflation rate and the GDPNow proxy. These inflation measures have differed by 5 percentage points or more on a number of occasions. Since goods imports are 12 percent of GDP, a miss of this magnitude on the price deflator would lead to a miss on the real net exports of goods contribution to growth of 0.5 percentage points or more, even if the other ingredients in the calculation were all correct.
Are there any lessons here for improving GDPNow? Ideally, GDPNow would be able to closely map the monthly source data to real goods net exports so that most of the weight would go to the bean counting forecast once all of the data are in—much as it does with nonresidential structures and residential investment. The BEA's estimates of real petroleum imports are based on similar data in the monthly international trade data publication. Because petroleum imports account for so much of the volatility of inflation for goods imports, it may be better to use the monthly real petroleum imports data directly and only worry about replicating the price index for nonpetroleum goods.
That said, a previous macroblog post illustrated that the method GDPNow currently uses has a reasonable forecasting track record for net exports when compared with several consensus estimates from professional forecasters. Net exports may remain difficult to nowcast even with refinements to GDPNow's methodology.
GDPNow has established a commendable track record. But sometimes when it misses the mark, an analysis of the error can provide insight into how GDPNow works and the limitations of the model.
May 23, 2016
Can Two Wrongs Make a Right?
In a recent macroblog post, I showed that forecasts from the Atlanta Fed's real gross domestic product (GDP) nowcasting model—GDPNow—have been about as accurate a forecast of the U.S. Bureau of Economic Analysis's (BEA) first estimate of real GDP growth as the consensus from the Wall Street Journal Economic Forecasting Survey. Because GDPNow essentially uses a "bean-counting" approach that tallies the forecasts of the various main subcomponents of GDP, the total GDP forecast error can be broken up into the forecast errors coming from each piece of GDP. For most of the subcomponents of GDP, the contribution to total GDP growth is approximately its real growth rate multiplied by its expenditure share of nominal GDP (the exact formulas are in the working paper for GDPNow). The following chart shows the subcomponent contributions to the GDPNow forecast errors since the third quarter of 2011. (I want to note that the forecast errors are based on the final GDPNow forecasts formed before the BEA's first estimates of GDP are released.)
The forecast errors for the subcomponents can sometimes be quite large. For example, for the fourth quarter of 2013, GDPNow underestimated the combined contributions of net exports and inventory investment by nearly 2 percentage points. However, these misses were nearly offset by overestimates of the other contributions to growth (consumption, business and residential fixed investment, and government spending).
The pattern of large but largely offsetting GDP subcomponent errors has been attributed to the work of a fictional "Saint Offset," as former Fed Governor Laurence Meyer noted in a 1998 speech. Unfortunately, "Saint Offset" doesn't always come to the forecaster's aid. For example, in the fourth quarter of 2011, GDPNow predicted 5.2 percent growth—well above the BEA's first estimate of 2.8 percent—and the subcomponent errors were predominantly on the high side.
A closer look at the chart also reveals that GDPNow has had a tendency to overestimate the contribution of business fixed investment to growth and underestimate the growth contribution of inventory investment. Although these subcomponent biases have nearly offset one another on average, we really don't want to have to rely on "Saint Offset." We would like the subcomponent forecasts to be reasonably accurate because the subcomponents of GDP are of interest in their own right.Have the subcomponent biases been a unique feature of GDPNow forecasts? It appears not. Both the Survey of Professional of Forecasters (SPF), conducted about 11 weeks prior to the first GDP release, and Blue Chip Economic Indicators, conducted as close as three weeks prior to the first release, provide consensus forecasts for some GDP subcomponents. The following table provides an average forecast error (as a measure of bias) and average absolute forecast error (as a measure of accuracy) of the subcomponent growth contributions for the two surveys and comparably timed GDPNow forecasts.
We see that the biases in GDPNow's subcomponents have been fairly similar to those in the two surveys. For example, all three sources have underestimated the average inventory investment contribution to growth by fairly similar magnitudes.
The relative accuracy of GDPNow's subcomponent and overall GDP forecasts has also been similar to the accuracy of the two surveys. "Saint Offset" has helped all three forecasters; the standard errors of the real GDP forecasts are 20 percent to 40 percent lower than they would be if the forecast errors of the subcomponents did not cancel each other out.
Finally, notice that some GDP subcomponents appear to be much more difficult to forecast than others. For instance, the bias and accuracy metrics for consumer spending are smaller than they are for inventory investment. This differential is not really that surprising, because more monthly source data are available prior to the first GDP release for consumer spending than for inventory investment.
Can we take any comfort in knowing that private forecasters have mirrored the biases in GDPNow's subcomponent forecasts? An optimistic interpretation is that the string of one-sided misses are the result of bad luck—an atypical sequence of shocks that neither GDPNow nor private forecasters could account for. A more troubling interpretation is that there have been structural changes in the economy that neither GDPNow nor the consensus of private forecasters have identified. Irrespective of the reason, though, optimal forecasts should be unbiased. If biases in some of the subcomponents continue, then forecasters will need to look for a robust way to eliminate them.
May 16, 2016
GDPNow and Then
Real-time forecasts from the Atlanta Fed’s real gross domestic product (GDP) nowcasting model—GDPNow—have been regularly updated since August 2011 (the model was introduced online in July 2014). So we now have a nearly five-year history to allow us to evaluate the accuracy of the model’s forecasts. The chart below shows forecasts from GDPNow (red dots) alongside actual first estimates of real GDP growth (gray bars) from the U.S. Bureau of Economic Analysis (BEA). For comparison, the blue dots in the chart are the consensus (average) forecasts from the Wall Street Journal Economic Forecasting Survey (WSJ Survey).
The initial estimate of real GDP growth for a particular quarter is usually published at the end of the subsequent month. The WSJ Survey consensus forecasts plotted above were released about two weeks before these estimates. To maintain comparable timing with the WSJ Survey, the GDPNow forecasts shown in the chart are those constructed on or before the 12th day of the same month.
Occasionally, there has been relatively large disagreement between GDPNow and the WSJ consensus. For example, GDPNow predicted that GDP growth would be below 0.5 percent for five out of 19 quarters between 2011 and 2016, and the lowest WSJ Survey consensus forecast for any of those quarters was 1.3 percent. Nonetheless, the average accuracy of the GDPNow and WSJ Survey consensus forecasts has been similar: the average absolute forecast error (average error without regard to sign) for GDPNow was 0.56 versus 0.60 for the WSJ Survey consensus.
Studies have shown that the average or median of a set of professional forecasts tends to be more accurate than an individual forecaster (see, for example, here and here). Therefore, it’s surprising that GDPNow has been about as accurate on average as the WSJ Survey consensus. To see just how surprising this result is, I used the fact that the WSJ Survey provides both the names and forecasts of its respondents. From these, I constructed a panel dataset with each respondent’s absolute forecast errors and their absolute disagreement (difference) from the consensus forecast. Using a standard econometric technique (a two-way fixed-effects regression), we can then calculate each panelist’s average absolute GDP forecast error and their average absolute disagreement with the WSJ Survey consensus. These points are shown in the scatterplot below.
There is a clear inverse relationship between average forecast accuracy and average disagreement with the WSJ Survey consensus. However, GDPNow’s accuracy and disagreement statistics do not fit the general pattern. GDPNow (the orange diamond in the chart) was more accurate on average than all but six out of 49 WSJ panelists, though at the same time it differed from the consensus by more on average than all but four of the panelists.
What should one infer from all of this? Differences in forecasting method could be part of the explanation. GDPNow differs from many other approaches to nowcasting in that it is essentially a “bean counting” exercise. It doesn’t use historical correlations of GDP with other economic series in the way that commonly used dynamic factor models do, and it also doesn’t incorporate judgmental adjustments (see here for more discussion of these differences). During a period when the economy has been giving very mixed signals, perhaps it doesn’t come as a surprise that GDPNow’s forecasts occasionally deviate quite a bit from the WSJ Survey consensus. Time will tell if GDPNow continues to perform at least as well as the consensus.
July 01, 2015
Far Away Yet Close to Home: Discussing the Global Economy's Effects
In case you needed any motivation to take interest in the outcome of ongoing negotiations between the Greek government and its international creditors, this excerpt from the Wall Street Journal ought to do it:
Global growth is really important. We are all connected through the financial markets, through foreign-exchange markets," Fed governor Jerome Powell said last week in an interview with The Wall Street Journal. "If global growth weakens, or remains weak, and we get into a trend of that, then yes, that will be a big headwind for the United States economy."
Last week, I participated in the latest edition of our webcast, ECONversations, devoted to the theme "what to make of the first quarter?" (The webcast can be found here). The conversation revolved around the Atlanta Fed staff's view of why 2015 began with such a whimper and ideas on prospects for improvement through the balance of the year.
Not surprisingly, the international context loomed large. Between June 2014 and March 2015, the U.S. dollar appreciated by about 14 percent against a broad basket of currencies, and by about 20 percent against major currencies. The dollar has roughly remained in those neighborhoods since. As to the gross domestic product (GDP) side of the story, arithmetically net exports subtracted almost 2 percentage points off first quarter growth.
A key assumption of our current outlook is that the international environment (including the exchange rate) will stabilize, and smoother sailing without the "big headwind" referenced by Governor Powell is ahead.
That assumption generated some discussion (in the Q&A part of the webcast, and via online questions). With some paraphrasing, here are a few of the comments and questions we received, and my best attempt to respond:
Q: You associate the prior appreciation in the dollar with a several percentage point subtraction from growth in the first quarter. This seems quite large in context of available research on the elasticity of the trade balance to movements in the foreign exchange value of the dollar.
A: In the webcast, I did loosely refer to the trade effect on first quarter GDP as a "dollar effect." But the questioner—Barclay's head of U.S. economics research, Michael Gapen— is completely correct in asserting that standard estimates wouldn't support exchange-rate appreciation as an all-encompassing explanation for the big first quarter trade deficit. Our own estimates imply that four quarters after an exchange rate shock that raises the real broad-dollar index by 10 percentage points, real GDP is about one-half a percentage point lower than it would have been without the shock. This impact is roughly the same as most standard estimates (including Barclay's).
Some analyses might imply a larger GDP impact for the pure dollar effect, but any reasonable estimate would leave a fair amount of the first quarter net export decline unexplained. In any event, exchange-rate movements are both cause and effect, which brings us to:
Q: I have a question regarding the impact of the U.S. dollar (USD) in the economy. We often learn that changes in the real exchange rate affect the economy with a lag. Take Japan, for instance. It had a substantial depreciation in Japanese yen (JPY) real exchange rate but with very minimal impact on Japan's trade performance so far. What makes you so confident that the strong USD has had a strong impact in the U.S. economy in such a short period of time? Wouldn't the negative contribution from net exports more likely be linked to delays in West Coast ports and the sharp slowdown in Asian economies (China, in particular)?
A: Yes, in our analysis (and most we know of), the effects of exchange rates occur with a lag. And, as noted above, only a fraction of the decline in net exports by the end of 2014 and into the beginning of this year can be plausibly attributed to dollar appreciation. But we do think those effects are there, and they are continuing (to a lesser extent) in the current quarter.
Of course, changes in the value of the currency are an effect of other developments as well as a cause of changes in exports, GDP, and the like. All else is not typically equal, which often makes simple correlations (or, in the Japanese case, the lack thereof) difficult to interpret.
One of those "not equal" things could well have been the port delays. We don't have a firm estimate of how the backlogs might have affected the first quarter GDP statistic. If the impact was indeed material, we should see some reversal in the second and third quarters now that things are apparently getting back to normal. We'll count that as an upside risk.
And looking forward?
Q: Shouldn't the economic crisis in Greece dampen the demand for American exports and decrease growth well into the fourth quarter?
A: The good news is that current forecasts suggest 2015 euro-area growth will exceed its 2014 pace (according to the World Bank). In fact, the 2015 forecast strengthened over the course of this year despite the ongoing uncertainty associated with the Greek crisis. By most accounts, Canadian economic activity this year is expected to follow a trajectory similar to the United States (in like a lamb, out like something less lambish).
Mexico, as well, is expected to show more growth this year than last, despite some softening of the outlook since the beginning of the year. Put those three together (expanding the euro area to the entire European Union), and you have the anticipation of some improvement in countries accounting for somewhere in the neighborhood of 55 percent of our export markets.
The bad news is the ongoing uncertainty associated with the Greek crisis. Further, the outlook in emerging economies is growing more downbeat. These realities—a continuing impact of prior dollar appreciation and the fact that better foreign growth still does not equate to great growth—has us reluctant to think that net exports will be a big positive number in this year's GDP calculations. That reluctance notwithstanding, for now we are writing in a smaller trade deficit over the course of the year than what we saw in the first quarter.
If you want to go into the July 4 holiday on a somewhat optimistic note, I'll note that our GDPNow estimates for the second quarter have strengthened substantially with the arrival of more recent data—notably including signals of a much lower trade deficit effect than in the first quarter and today's positive news on manufacturing and nonresidential construction. Those data may not be enough to generate full confidence in our forecast for a much better second half of 2015, but they are moving in the right direction.
April 20, 2015
What the Weather Wrought
At Seeking Alpha, Joseph Calhoun responds to Friday's macroblog post, which noted that, over the course of the recovery, first-quarter gross domestic product (GDP) growth has on average been slower than the quarterly performance over the balance of the year:
... the "between-the-lines" meaning of the Atlanta post is to ignore all of this since this weakness is being portrayed as "just like last year" a statistical problem in the one measure that economists think most represents the economy.
Rest assured, we try pretty hard to not place any messages "between the lines," and the penultimate sentence of Friday's piece was meant to strike the appropriately tentative tone: "As for the rest of the year, we'll have to wait and see."
We do believe, like others, that weather was at play in the subpar performance of 2015's debut. Severe weather, in February in particular, can explain some of the first-quarter weakness, but "some" is the operative qualifier.
As the following chart illustrates, relative to a baseline forecast without weather effects—proxied with National Oceanic and Atmospheric Administration measures of heating and cooling days through March—we estimate that the severity of the winter subtracted about 0.6 percentage point from GDP growth:
Two points: First, to the extent that weather is a culprit in subpar first-quarter growth, we should see some payback in the current quarter (as, dare we say, we saw last year).
Second, we (the Atlanta Fed staff) did not begin the year projecting first-quarter growth at a mere 1.8 percent annualized (as the benchmark forecast in the experiment illustrated above implies). That rate of growth is a considerable step-down from our forecast at the beginning of the year, forced by the realities of the incoming data (as captured, for example, by GDPNow estimates). That gap leaves plenty of explaining left to do.
Observable developments can plausibly explain much of the forecast miss—mainly the initial, somewhat ambiguous, impact of energy price declines and the rapid, steep appreciation of the dollar, which has clearly been associated with a suppression of export activity. Our current view is that, as energy prices and the exchange rate stabilize, we will see a return to growth patterns that are closer to 3 percent than 1 percent.
We are not, however, selling the position that it is wise to be completely sanguine about the rest of the year. Here is the official word from Dennis Lockhart, president of the Atlanta Fed (subscription required for full citation):
I lean to a later lift-off date [for the federal funds rate target]. To the extent you want to simplify that debate to June versus September, I lean to September. I don't think, given the progress we have made, the state of the economy, and my confidence that the first quarter was an aberration, that it would be horribly damaging to go a little earlier versus later. But my preference would be to wait for more confirming evidence that we are on the track we think we are on and we expect to carry us back to inflation toward target.
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