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Report NEP-BIG-2017-12-03
This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-BIG
The following items were announced in this report:
- Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2017, "Macroeconomic nowcasting and forecasting with big data," Staff Reports, Federal Reserve Bank of New York, number 830, Nov.
- Kazuyuki MOTOHASHI, 2017, "Survey of Big Data Use and Innovation in Japanese Manufacturing Firms," Policy Discussion Papers, Research Institute of Economy, Trade and Industry (RIETI), number 17027, Aug.
- Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2017, "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Working Papers, National Bureau of Economic Research, Inc, number 24001, Nov.
- Wu, Wenjie & Wang, Jianghao & Li, Chengyu & Wang, Mark, 2016, "The geography of city liveliness and consumption: evidence from location-based big data," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 83642, Nov.
- Mitsuru Igami, 2017, "Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo," Papers, arXiv.org, number 1710.10967, Oct, revised Mar 2018.
- Lester Mackey & Vasilis Syrgkanis & Ilias Zadik, 2017, "Orthogonal Machine Learning: Power and Limitations," Papers, arXiv.org, number 1711.00342, Nov, revised Aug 2018.
- Mariel McKenzie Finucane & Ignacio Martinez & Scott Cody, , "What Works for Whom? A Bayesian Approach to Channeling Big Data Streams for Public Program Evaluation," Mathematica Policy Research Reports, Mathematica Policy Research, number 982eef5914cb4e39b91da7114.