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.