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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.
[ Enlarge ]
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.
August 9, 2007
Just in case it isn't completely obvious, macroblog is on a temporary hiatus as I make the transition to my new position at the Federal Reserve Bank of Atlanta. For those of you who have asked -- and really, thanks so much for asking -- this blog will indeed live on. Hope you hang tight, and don't delete me from your feeds -- I'll be back in action before you know it,
June 21, 2007
Dark Matter By Any Other Name
... The United States miracle of the 1990s was that our productivity began growing faster than that of other countries, even though we were the richest to start with...
To explain the experience in the United States, one would have to believe that Americans have some better way of translating the new technology into productivity than other countries. And that is precisely what [London School of Economics] Professor [John] Van Reenen’s research suggests.
His paper “Americans Do I.T. Better: U.S. Multinationals and the Productivity Miracle,” (with Nick Bloom of Stanford University and Raffaella Sadun of the London School of Economics) looked at the experience of companies in Britain that were taken over by multinational companies with headquarters in other countries. They wanted to know if there was any evidence that the American genius with information technology transfers to locations outside the United States. If American companies turn computers into productivity better than anyone else, can businesses in Britain do the same when they are taken over by Americans?
And in the huge service sectors — financial services, retail trade, wholesale trade — they found compelling evidence of exactly that. American takeovers caused a tremendous productivity advantage over a non-American alternative.
When Americans take over a business in Britain, the business becomes significantly better at translating technology spending into productivity than a comparable business taken over by someone else. It is as if the invisible hand of the American marketplace were somehow passing along a secret handshake to these firms.
Sound familiar? If you can't quite put your finger on it, here's a refresher from Ricardo Hausmann and Federico Sturzenegger:
There is a large difference between our view of the US as a net creditor with assets of about 600 billion US dollars and BEA’s view of the US as a net debtor with total net debt of 2.5 trillion. We call the difference between these two equally arbitrary estimates dark matter, because it corresponds to assets that we know exist, since they generate revenue but cannot be seen (or, better said, cannot be properly measured)...
At least three factors account for the accumulation of dark matter. The first refers to foreign direct investment (FDI). Consider a simple example. Imagine the construction of EuroDisney at the cost of 100 million (the numbers are imaginary). Imagine also, for the sake of the argument that these resources were borrowed abroad at, say, a 5% rate of return. Once EuroDisney is in operation it yields 20 cents on the dollar. The investment generates a net income flow of 15 cents on the dollar but the BEA would say that the net foreign assets position would be equal to zero. We would say that EuroDisney in reality is not worth 100 million (what BEA would value it) but four times that (the capitalized value at our 5% rate of the 20 million per year that it earns). BEA is missing this and therefore grossly understates net assets. Why can EuroDisney earn such a return? Because the investment comes with a substantial amount of know-how, brand recognition, expertise, research and development and also with our good friends Mickey and Donald. This know-how is a source of dark matter. It explains why the US can earn more on its assets than it pays on its liabilities and why foreigners cannot do the same. We would say that the US exported 300 million in dark matter and is making a 5 percent return on it. The point is that in the accounting of FDI, the know-how than makes investments particularly productive is poorly accounted for.
That story might only go so far, as the Federal Reserve Bank of New York's Matthew Higgins, Thomas Klitgaard, and Cedric Tille claim...
... we review the argument that the United States holds large amounts of intangible assets not captured in the data—assets that would bring the true U.S. net investment position close to balance. We argue that intangible capital, while a relevant dimension of economic analysis, is unlikely to be substantial enough to alter the U.S. net liability position.
... but it's apparently more than a fairy tale.
June 20, 2007
Apples To Apples
Today at Angry Bear, my friend pgl is doing some back-of-the-envelope econometrics:
From 1980QIV to 1992QIV, average annual real GDP growth = 3.0%.
From 1992QIV to 2000QIV, average annual real GDP growth = 3.6%.
From 2000QIV to 2006QIV, average annual real GDP growth = 2.6%.
Notice something? During the low tax eras (Reagan-Bush41 and Bush43), we witnessed lower growth rates. During the Clinton Administration – which began with its fiscally responsible policies with a tax rate increase – we saw strong growth. Maybe part of the explanation has to do with the impact on national savings from fiscal irresponsibility justified by phony free lunch promises.
I have a bit of a problem with the evidence here. To get the gist of my objection, take the following quiz:
Which one of these time periods did not include a recession?
a. 1980QIV to 1992QIV
b. 1992QIV to 2000QIV
c. 2000QIV to 2006QIV
If you answered b, you win the gold star. And if you knew that, are you really surprised that the period from 1992 through 2000 had higher average growth than the other two periods, which did include recessions? Suppose we instead make the comparisons including only the expansion years of the Reagan-Bush41 and Bush43 administrations? Here's what you get:
From 1983 to 1989, average annual real GDP growth = 4.3%.
From 1992 to 2000, average annual real GDP growth = 3.7%.
From 2002 to 2006, average annual real GDP growth = 2.9%.
You could just as well look at those numbers and conclude that potential GDP growth -- measured cycle to cycle -- is declining through time. And if you accept pgl's characterization of irresponsible policy, followed by responsible policy, followed by irresponsble policy, you might then conclude that policy has very little to do with that trend.
Perhaps you would want to argue that I shouldn't exclude recessions because the absence of a downturn in the 1992-2000 period is itself evidence of the superior growth effects of the fiscally responsible policies of the Clinton administration? Let me try to talk you out of that with a few more questions:
1. Do you really want to blame the Reagan fiscal policies for the 1980-82 recessions -- which are almost universally attributed to the Volcker Fed's fight against double digit inflation inherited from the policies of the 1970s?
2. Do you really want to characterize Bush41 as a tax cutter? And would you maintain that position knowing that Clinton's major piece of fiscal policy -- the Omnibus Reconciliation Act of 1993 --was pretty much of copy of the Omnibus Reconciliation Act of 1990, the legislation in which President Bush the Elder famously broke his "no new taxes" pledge?
3. Do you really want to finger the Bush43 tax cuts for the 2001 recession which began a scant two months into the administration and was over even before the tax cuts took effect?
Look -- It might very well be that "fiscal responsibility," as pgl defines it, is a central ingredient of pro-growth policy. But those GDP comparisons don't make the point.
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