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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.

Authors for Policy Hub: Macroblog are Dave Altig, John Robertson, and other Atlanta Fed economists and researchers.

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November 7, 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 surveyOff-site link 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).

chart-one

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.

chart-two

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 1, 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.