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 issued this report. It is usually issued weekly.

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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 830, Federal Reserve Bank of New York.
• MOTOHASHI Kazuyuki, 2017. "Survey of Big Data Use and Innovation in Japanese Manufacturing Firms," Policy Discussion Papers 17027, Research Institute of Economy, Trade and Industry (RIETI).
• Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2017. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Working Papers 24001, National Bureau of Economic Research, Inc.
• 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 83642, London School of Economics and Political Science, LSE Library.
• Mitsuru Igami, 2017. "Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo," Papers 1710.10967, arXiv.org, revised Mar 2018.
• Lester Mackey & Vasilis Syrgkanis & Ilias Zadik, 2017. "Orthogonal Machine Learning: Power and Limitations," Papers 1711.00342, arXiv.org, revised Aug 2018.
• Mariel McKenzie Finucane & Ignacio Martinez & Scott Cody, "undated". "What Works for Whom? A Bayesian Approach to Channeling Big Data Streams for Public Program Evaluation," Mathematica Policy Research Reports 982eef5914cb4e39b91da7114, Mathematica Policy Research.