Big Data and Economic Nowcasting

Check out this informative paper from the Federal Reserve Bank of New York: "Macroeconomic Nowcasting and Forecasting with Big Data", by Brandyn Bok, Daniele Caratelli, Domenico Giannone, Argia Sbordone, and Andrea Tambalotti.

Key methods for confronting big data include (1) imposition of restrictions (for example, (a) zero restrictions correspond to "sparsity", (b) reduced-rank restrictions correspond to factor structure, etc.), and (2) shrinkage (whether by formal Bayesian approaches or otherwise).

Bok et al. provide historical perspective on use of (1)(b) for macroeconomic nowcasting; that is, for real-time analysis and interpretation of hundreds of business-cycle indicators using dynamic factor models. They also provide a useful description of FRBNY's implementation and use of such models in policy deliberations.

It is important to note that the Bok et al. approach nowcasts current-quarter GDP, which is different from nowcasting "the business cycle" (as done using dynamic factor models at FRB Philadelphia, for example), because GDP alone is not the business cycle. Hence the two approaches are complements, not substitutes, and both are useful.

Hazard Functions for U.S. Expansions

Glenn Rudebusch has a very nice 2016 FRBSF Letter, "Will the Economic Recovery Die of Old Age?".  He draws on perspective and results from our joint work of 25 years ago (including a paper we did with Dan Sichel -- see below), and he applies them to the present expansion.  He correctly emphasizes that U.S. expansion hazard functions are basically flat, so "old" expansions are no more likely to end than "young" ones. That's of some comfort, since the present expansion, which started in mid-2009, is getting long in the tooth!

Actually, the flat expansion hazard is only for post-WWII expansions; the prewar expansion hazard is sharply increasing. Here's how they compare (copied from Glenn's FRBSFLetter):

Probability of an Expansion ending within a month

Perhaps the massive difference is due to "good policy", that is, post-war policy success in "keeping expansions alive".  Or perhaps it's just "good luck" -- but it's so big and systematic that luck alone seems an unlikely explanation.

For more on all this, and to see the equally-fascinating and very different results for recession hazards, see Diebold, Rudebusch and Sichel (1992), which I consider to be the best statement of our work in the area.

[Footnote:  I wrote this post about three days ago, intending to release it next week. I just learned that The Economist (May 21st issue) also reports on the Rudebusch FRBSF Letter (see http://www.economist.com/news/finance-and-economics/21699124-when-periods-economic-growth-come-end-old-age-rarely-blame-murder), so I'm releasing it early.  Interesting that both The Economist and I are not only slow -- Glenn sent me his Letter in February, when it was published! -- but also identically slow.]

The Distribution of Global Economic Activity...

... as proxied by the global distribution of nighttime lights (from a fascinating new paper by Hendersen et al.).  Like many good graphics, this one repays careful study.  You'll see lots of places where the lights match your prior, but you'll also see places that are perhaps "surprisingly" well-lit relative to popular perceptions (e.g., central America), other places that are perhaps surprisingly dark (e.g., most of Russia), fascinating patterns (e.g., look at Europe stretching east into Russia), etc.

On the Real-Time GDP War

A few days ago the WSJ did an interesting piece, Fed Banks Spar Over GDP Data, highlighting that the "race to provide credible real-time data on U.S. economic growth is pitting the Federal Reserve Bank of New York against its sibling in Atlanta."

In all this, real-time data on "economic growth" is interpreted as real-time data on GDP growth.

In my opinion, all of the real-time GDP products basically reflect a misguided perspective if the goal is real-time tracking of economic growth (which is as it should be, and what is claimed). If you want to track real-time growth, you should be tracking an extraction of a broad dynamic factor, effectively averaging over many indicators, not just tracking real-time GDP. That has been the leading and invaluable perspective from Burns and Mitchell straight through to modern dynamic-factor approaches.  My favorite, of course, is the FRB Philadelphia's ADS Index, but there are many others.

Eurostat Forecasting Competition Deadline Approaching

I have some serious reservations about forecasting competitions, at least as typically implemented by groups like Kaggle. But still they're useful and exciting and absolutely fascinating. Here's a timely call for participation, from Eurostat. (Actually this one is nominally for nowcasting, not forecasting, but in reality they're the same thing.) 

[I'm not sure why they're trying to shoehorn "big data" into it, except that it sounds cool and everyone wants to jump on the bandwagon. The winner is the winner, whether based on big data, small data, or whatever, and whether produced by an econometrician, a statistician, or a data scientist. I'm not even sure what "Big Data" means, or what a "data scientist" means, here or anywhere. (Standard stat quip: A data scientist is a statistician who lives in San Francisco.) End of rant.]

Big Data for Official Statistics Competition launched - please register by 10 January 2016

 

The Big Data for Official Statistics Competition (BDCOMP) has just been launched, and you are most welcome to participate. All details are provided in the call for participation:

http://www.cros-portal.eu/content/call-participation

Participation is open to everybody (with a few very specific exceptions detailed in the call).

In this first instalment of BDCOMP, the competition is exclusively about nowcasting economic indicators at national or European level.

There are 7 tracks in the competition. They correspond to 4 main indicators: Unemployment, HICP, Tourism and Retail Trade and some of their variants.

Usage of Big Data is encouraged but not mandatory. For a detailed description of the competition tasks, please refer to the call.

The authors of the best-performing submissions for each track will be invited to present their work at the NTTS 2017 conference (the exact award criteria can be found in the call).

The deadline for registration is 10 January 2016. The duration of the competition is roughly a year (including about a month for evaluation). For a detailed schedule of submissions, please refer to the call.

The competition is organised by Eurostat and has a Scientific Committee composed of colleagues from various member and observer organisations of the European Statistical System (ESS).

http://www.cros-portal.eu/content/scientific-committee-1

On the behalf of the BDCOMP Scientific Committee,

The BDCOMP organising team