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Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data

Listed author(s):
  • Serena Ng

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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 23673.

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Date of creation: Aug 2017
Handle: RePEc:nbr:nberwo:23673
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  1. Jessie Handbury & Tsutomu Watanabe & David E. Weinstein, 2013. "How Much Do Official Price Indexes Tell Us about Inflation?," NBER Working Papers 19504, National Bureau of Economic Research, Inc.
  2. Jonathan H. Wright, 2013. "Unseasonal Seasonals?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(2 (Fall)), pages 65-126.
  3. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
  4. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-368, July.
  5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, 06.
  6. Pierce, David A & Grupe, Michael R & Cleveland, William P, 1984. "Seasonal Adjustment of the Weekly Monetary Aggregates: A Model-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 260-270, July.
  7. Christian Broda & Ephraim Leibtag & David E. Weinstein, 2009. "The Role of Prices in Measuring the Poor's Living Standards," Journal of Economic Perspectives, American Economic Association, vol. 23(2), pages 77-97, Spring.
  8. Jonathan H. Wright, 2013. "Unseasonal Seasonals?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(2 (Fall)), pages 65-126.
  9. Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher RĂ© & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
  10. Judith A. Chevalier & Anil K. Kashyap & Peter E. Rossi, 2003. "Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data," American Economic Review, American Economic Association, vol. 93(1), pages 15-37, March.
  11. Athey, Susan & Imbens, Guido W., 2015. "Machine Learning for Estimating Heterogeneous Causal Effects," Research Papers 3350, Stanford University, Graduate School of Business.
  12. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
  13. Olivier Coibion & Yuriy Gorodnichenko & Gee Hee Hong, 2015. "The Cyclicality of Sales, Regular and Effective Prices: Business Cycle and Policy Implications," American Economic Review, American Economic Association, vol. 105(3), pages 993-1029, March.
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