The Data Revolution and Economic Analysis
Many believe that “big data” will transform business, government and other aspects of the economy. In this article we discuss how new data may impact economic policy and economic research. Large-scale administrative datasets and proprietary private sector data can greatly improve the way we measure, track and describe economic activity. They also can enable novel research designs that allow researchers to trace the consequences of different events or policies. We outline some of the challenges in accessing and making use of these data. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in economics.
|Date of creation:||May 2013|
|Date of revision:|
|Contact details of provider:|| Postal: 366 Galvez Street, Stanford, California 94305-6015|
Phone: (650) 725-1874
Fax: (650) 723-8611
Web page: http://siepr.stanford.edu
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Liran Einav & Theresa Kuchler & Jonathan D. Levin & Neel Sundaresan, 2011.
"Learning from Seller Experiments in Online Markets,"
NBER Working Papers
17385, National Bureau of Economic Research, Inc.
- Liran Einav & Theresa Kuchler & Jonathan Levin & Neel Sundaresan, 2011. "Learning from Seller Experiements in Online Markets," Discussion Papers 10-033, Stanford Institute for Economic Policy Research.
- Alexandre Belloni & D. Chen & Victor Chernozhukov & Christian Hansen, 2010.
"Sparse models and methods for optimal instruments with an application to eminent domain,"
CeMMAP working papers
CWP31/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
- 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.
- Alberto Cavallo, 2015. "Scraped Data and Sticky Prices," NBER Working Papers 21490, National Bureau of Economic Research, Inc.
- Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2011. "The Long-Term Impacts of Teachers: Teacher Value-Added and Student Outcomes in Adulthood," NBER Working Papers 17699, National Bureau of Economic Research, Inc.
- Peter J. Klenow & Oleksiy Kryvtsov, 2008. "State-Dependent or Time-Dependent Pricing: Does it Matter for Recent U.S. Inflation?," The Quarterly Journal of Economics, Oxford University Press, vol. 123(3), pages 863-904.
When requesting a correction, please mention this item's handle: RePEc:sip:dpaper:12-017. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anne Shor)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.