Note well the important lesson: both the ADS Index (designed for real-time analysis of broad real activity) and GDPplus (designed mostly for historical analysis of real GDP, an important part of real activity) reduce, if not eliminate, measurement error by "averaging it out."
All told, ADS paints a clear picture: conditional on the underlying indicator data available now, real growth appears to be typical (ADS is constructed so that 0 corresponds to average growth) -- not especially strong, but simultaneously, not especially weak.
As discussed in an earlier post, my co-authors and I believe that our "GDPplus," obtained by optimally blending the noisy expenditure- and income-side GDP estimates, provides a superior U.S. GDP measure. (Check it out online; the Federal Reserve Bank of Philadelphia now calculates and reports it.) A few days ago we revised and re-posted the working paper on which it's based (Aruoba, Diebold, Nalewaik, Schorfheide, and Song, "Improving GDP Measurement: A Measurement Error Perspective," Manuscript, University of Maryland, Federal Reserve Board and University of Pennsylvania, Revised June 2014).
It's important to note that GDPplus is not simply a convex combination of the expenditure- and income-side estimates; rather, it is produced via the Kalman filter, which averages optimally over both space and time. Hence, although GDPplus is usually between the expenditure- and income-side estimates, it need not be. Presently we're in just such a situation, as shown in the graph below. 2014Q1 real growth as measured by GDPplus (in red) is well above both of the corresponding expenditure- and income-side GDP growth estimates (in black), which are almost identical.
Ulrich Mueller and Mark Watson's "Measuring Uncertainty About Long-Run Predictions" is important and original. I like it more (and understand it more) every time I see it. The latest was last week in Frankfurt at the ECB's Eighth Annual Workshop on Forecasting. No sense transcribing my discussion; just view it directly.
"Piketty's book truly shines on the data side. ... Its tables and figures...provide a rich and jaw-dropping image, like a new high-resolution photo of a previously-unseen galaxy. I'm grateful to Piketty for sending it our way, for heightening awareness, and for raising important questions."
Measurement endorsements don't come much stronger.
Then I did a 180. Upon belatedly reading the Financial Times' Piketty piece, I felt I'd been had, truly had. Out poured my second Piketty Post, written in a near-rage, without time to digest Piketty's response.
Now, with the benefit of more time to read, re-read, and reflect, yes, I'm doing another 180. It seems clear that the bulk of the evidence suggests that the FT, not Piketty, is guilty of sloppiness. Piketty's response is convincing, and all-told, his book appears to remain a model of careful social-science measurement (thoughtful discussion, meticulous footnotes, detailed online technical appendix, freely-available datasets, etc. -- see his website).
Ironically, then, as the smoke clears, my first Piketty post remains an accurate statement of my views.
In my earlier Piketty post, I wrote, "If much of its "reasoning" is little more than neo-Marxist drivel, much of its underlying measurement is nevertheless marvelous." The next day, recognizing the general possibility of a Reinhart-Rogoff error, but with no suspicion that that anything was actually remiss, I added "(assuming of course that it's trustworthy)."
Perhaps I really should read some newspapers. Thanks to Boragan Aruoba for noting this, and for educating me. Turns out that the Financial Times -- clearly a centrist publication with no ax to grind -- got hold of Piketty's data (underlying source data, constructed series, etc.) and published a scathing May 23 indictment.
The chart above -- just one example -- is from The Economist, reporting on the FT piece. Somehow Piketty managed to fit the dark blue curves to the light blue dots of source data. Huh? Sure looks like he conveniently ignored a boatload of recent data that happen to work against him. Put differently, his fits appear much more revealing of his sharp prior view than of data-based information. Evidently he forgot to talk about that in his book.
In my view, Reinhart-Rogoff was a one-off and innocent (if unfortunate) mistake, whereas the FT analysis clearly suggests that Piketty's "mistakes," in contrast, are systematic and egregious.