Sunday, August 20, 2017

Bayesian Random Projection (More on Terabytes of Economic Data)

Some additional thoughts related to Serena Ng's World Congress piece (earlier post here, with a link to her paper):

The key newish dimensionality-reduction strategies that Serena emphasizes are random projection and leverage score sampling.  In a regression context both are methods for optimally approximating an NxK "X matrix" with an Nxk X matrix, where k<<K. They are very different and there are many issues. Random projection delivers a smaller X matrix with columns that are linear combinations of those of the original X matrix, as for example with principal-component regression, which can sometimes make for difficult interpretation.  Leverage score sampling, in contrast, delivers a smaller X matrix with columns that are simply a subset of those of those of the original X matrix, which feels cleaner but has issues of its own.

Anyway, a crucial observation is that for successful predictive modeling we don't need deep interpretation, so random projection is potentially just fine -- if it works, it works, and that's an empirical matter.  Econometric extensions  (e.g., to VAR's) and evidence (e.g., to macro forecasting) are just now emerging, and the results appear encouraging.  An important recent contribution in that regard is Koop, Korobilis, and Pettenuzzo (in press), which significantly extends and applies earlier work of Guhaniyogi and Dunson (2015) on Bayesian random projection ("compression").  Bayesian compression fits beautifully in a MCMC framework (again see Koop et al.), including model averaging across multiple random projections, attaching greater weight to projections that forecast well.  Very exciting!

Monday, August 14, 2017

Analyzing Terabytes of Economic Data

Serena Ng's World Congress piece is out as an NBER w.p.  It's been floating around for a long time, but just in case you missed it, it's a fun and insightful read:

Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data
by Serena Ng  -  NBER Working Paper #23673.
http://papers.nber.org/papers/w23673


(Ungated copy at http://www.columbia.edu/~sn2294/papers/sng-worldcongress.pdf)

Abstract:

This paper seeks to better understand what makes big data analysis different, what we can and cannot do with existing econometric tools, and what issues need to be dealt with in order to work with the data efficiently.  As a case study, I set out to extract any business cycle information that might exist in four terabytes of weekly scanner data.  The main challenge is to handle the volume, variety, and characteristics of the data within the constraints of our computing environment. Scalable and efficient algorithms are available to ease the computation burden, but they often have unknown statistical properties and are not designed for the purpose of efficient estimation or optimal inference.  As well, economic data have unique characteristics that generic algorithms may not accommodate.  There is a need for computationally efficient econometric methods as big data is likely here to stay.

Saturday, August 12, 2017

On Theory, Measurement, and Lewbel's Assertion

Arthur Lewbel, insightful as always, asserts in a recent post that:
The people who argue that machine learning, natural experiments, and randomized controlled trials are replacing structural economic modeling and theory are wronger than wrong.
As ML and experiments uncover ever more previously unknown correlations and connections, the desire to understand these newfound relationships will rise, thereby increasing, not decreasing, the demand for structural economic theory and models.
I agree.  New measurement produces new theory, and new theory produces new measurement -- it's hard to imagine stronger complements.  And as I said in an earlier post,
Measurement and theory are rarely advanced at the same time, by the same team, in the same work. And they don't need to be. Instead we exploit the division of labor, as we should. Measurement can advance significantly with little theory, and theory can advance significantly with little measurement. Still each disciplines the other in the long run, and science advances.
The theory/measurement pendulum tends to swing widely.  If the 1970's and 1980's were a golden age of theory, recent decades have witnessed explosive advances in measurement linked to the explosion of Big Data.  But Big Data presents both measurement opportunities and pitfalls -- dense fogs of "digital exhaust" -- which fresh theory will help us penetrate.  Theory will be back.

[Related earlier posts:  "Big Data the Big Hassle" and "Theory gets too Much Respect, and Measurement Doesn't get Enough"]

Saturday, August 5, 2017

Commodity Connectedness


Forthcoming paper here
We study connectedness among the major commodity markets, summarizing and visualizing the results using tools from network science.

Among other things, the results reveal clear clustering of commodities into groups closely related to the traditional industry taxonomy, but with some notable differences.


Many thanks to Central Bank of Chile for encouraging and supporting the effort via its 2017 Annual Research Conference.