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Report NEP-BIG-2020-09-28
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:
- Mihail Caradaica, 2020, "Inequality and Artificial Intelligence in European Union," Proceedings of International Academic Conferences, International Institute of Social and Economic Sciences, number 10612985, Jul.
- Otchia, Christian & Asongu, Simplice, 2019, "Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images," MPRA Paper, University Library of Munich, Germany, number 101524, Jan.
- Rossouw, Stephanie & Greyling, Talita, 2020, "Big Data and Happiness," GLO Discussion Paper Series, Global Labor Organization (GLO), number 634.
- Maximilian Schäfer & Geza Sapi, 2020, "Learning from Data and Network Effects: The Example of Internet Search," Discussion Papers of DIW Berlin, DIW Berlin, German Institute for Economic Research, number 1894.
- Gries, Thomas & Naudé, Wim, 2020, "Artificial Intelligence, Income Distribution and Economic Growth," IZA Discussion Papers, IZA Network @ LISER, number 13606, Aug.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2020, "Prévision de l’activité économique au Québec et au Canada à l’aide des méthodes Machine Learning," CIRANO Project Reports, CIRANO, number 2020rp-18, Aug.
- Li, Sheng & Wu, Feng & Guan, Zhengfei, 2020, "Machine learning techniques for strawberry yield forecasting," 2020 Annual Meeting, July 26-28, Kansas City, Missouri, Agricultural and Applied Economics Association, number 304502, Jul, DOI: 10.22004/ag.econ.304502.
- Nathalia Montoya & Sebastián Nieto-Parra & Jose René Orozco & Juan Vázquez Zamora, 2020, "Using Google data to understand governments’ approval in Latin America," OECD Development Centre Working Papers, OECD Publishing, number 343, Sep, DOI: 10.1787/89ed5e8f-en.
- Pahmeyer, Christoph & Kuhn, Till & Britz, Wolfgang, , "‘Fruchtfolge’: A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling," Discussion Papers, University of Bonn, Institute for Food and Resource Economics, number 305287, DOI: 10.22004/ag.econ.305287.
- Jean-Sebastien Lacam, 2020, "Data: A collaborative ?
[Données: une stratégie collaborative?]," Post-Print, HAL, number hal-02930902, May, DOI: 10.1016/j.hitech.2020.100370. - Biewen, Martin & Kugler, Philipp, 2020, "Two-Stage Least Squares Random Forests with an Application to Angrist and Evans (1998)," IZA Discussion Papers, IZA Network @ LISER, number 13613, Aug.
- Christine Balagué, 2019, "Technologies numériques, intelligence artificielle et responsabilité," Post-Print, HAL, number hal-02907065, Oct.
- Obradovich, Nick & Özak, Ömer & Martín, Ignacio & Ortuño-Ortín, Ignacio & Awad, Edmond & Cebrián, Manuel & Cuevas, Rubén & Desmet, Klaus & Rahwan, Iyad & Cuevas, Ángel, 2020, "Expanding the measurement of culture with a sample of two billion humans," SocArXiv, Center for Open Science, number qkf42, Sep, DOI: 10.31219/osf.io/qkf42.
- Xiong, Tao & Ji, Yongjie & Ficklin, Darren, 2020, "What A Deep Learning Approach Say about Future US Soybean Yields," 2020 Annual Meeting, July 26-28, Kansas City, Missouri, Agricultural and Applied Economics Association, number 304452, Jul, DOI: 10.22004/ag.econ.304452.
- Mykola Babiak & Jozef Barunik, 2020, "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers, arXiv.org, number 2009.03394, Sep, revised Feb 2026.
- Martin Beraja & David Y. Yang & Noam Yuchtman, 2020, "Data-intensive Innovation and the State: Evidence from AI Firms in China," NBER Working Papers, National Bureau of Economic Research, Inc, number 27723, Aug.