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Report NEP-BIG-2019-10-21
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:
- Drew Fudenberg & Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2019, "Measuring the Completeness of Theories," Papers, arXiv.org, number 1910.07022, Oct.
- Caroline Paunov & Sandra Planes-Satorra & Greta Ravelli, 2019, "Review of national policy initiatives in support of digital and AI-driven innovation," OECD Science, Technology and Industry Policy Papers, OECD Publishing, number 79, Oct, DOI: 10.1787/15491174-en.
- Laura Barbieri & Chiara Mussida & Mariacristina Piva & Marco Vivarelli, 2019, "Testing the employment impact of automation, robots and AI: A survey and some methodological issues," DISCE - Working Papers del Dipartimento di Politica Economica, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE), number dipe0006, Sep.
- Smith, Gary, 2019, "The Paradox of Big Data," Economics Department, Working Paper Series, Economics Department, Pomona College, number 1003, Jan, revised 04 Jun 2019.
- Vinci Chow, 2019, "Predicting Auction Price of Vehicle License Plate with Deep Residual Learning," Papers, arXiv.org, number 1910.04879, Oct.
- L. Jason Anastasopoulos, 2019, "Principled estimation of regression discontinuity designs," Papers, arXiv.org, number 1910.06381, Oct, revised May 2020.
- Maas, Benedikt, 2019, "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper, University Library of Munich, Germany, number 96408, Sep.
- Bolte, Jérôme & Castera, Camille & Pauwels, Edouard & Févotte, Cédric, 2019, "An Inertial Newton Algorithm for Deep Learning," TSE Working Papers, Toulouse School of Economics (TSE), number 19-1043, Oct.
- Deli Chen & Yanyan Zou & Keiko Harimoto & Ruihan Bao & Xuancheng Ren & Xu Sun, 2019, "Incorporating Fine-grained Events in Stock Movement Prediction," Papers, arXiv.org, number 1910.05078, Oct.
- Bolte, Jérôme & Pauwels, Edouard, 2019, "Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning," TSE Working Papers, Toulouse School of Economics (TSE), number 19-1044, Oct.
- Smith, Gary, 2019, "Be Wary of Black-Box Trading Algorithms," Economics Department, Working Paper Series, Economics Department, Pomona College, number 1007, Jan, revised 04 Jun 2019.