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 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:

• Mihail Caradaica, 2020. "Inequality and Artificial Intelligence in European Union," Proceedings of International Academic Conferences 10612985, International Institute of Social and Economic Sciences.
• Otchia, Christian & Asongu, Simplice, 2019. "Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images," MPRA Paper 101524, University Library of Munich, Germany.
• Rossouw, Stephanie & Greyling, Talita, 2020. "Big Data and Happiness," GLO Discussion Paper Series 634, Global Labor Organization (GLO).
• Maximilian Schäfer & Geza Sapi, 2020. "Learning from Data and Network Effects: The Example of Internet Search," Discussion Papers of DIW Berlin 1894, DIW Berlin, German Institute for Economic Research.
• Gries, Thomas & Naudé, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," IZA Discussion Papers 13606, Institute of Labor Economics (IZA).
• 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 2020rp-18, CIRANO.
• Li, Sheng & Wu, Feng & Guan, Zhengfei, 2020. "Machine learning techniques for strawberry yield forecasting," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304502, Agricultural and Applied Economics Association.
• Nathalia Montoya & Sebastián Nieto-Parra & René Orozco & Juan Vázquez Zamora, 2020. "Using Google data to understand governments’ approval in Latin America," OECD Development Centre Working Papers 343, OECD Publishing.
• Pahmeyer, Christoph & Kuhn, Till & Britz, Wolfgang, 2020. "‘Fruchtfolge’: A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling," Discussion Papers 305287, University of Bonn, Institute for Food and Resource Economics.
• Jean-Sebastien Lacam, 2020. "Data: A collaborative ? [Données: une stratégie collaborative?]," Post-Print hal-02930902, HAL.
• Biewen, Martin & Kugler, Philipp, 2020. "Two-Stage Least Squares Random Forests with an Application to Angrist and Evans (1998)," IZA Discussion Papers 13613, Institute of Labor Economics (IZA).
• Christine Balagué, 2019. "Technologies numériques, intelligence artificielle et responsabilité," Post-Print hal-02907065, HAL.
• 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 qkf42, Center for Open Science.
• 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 304452, Agricultural and Applied Economics Association.
• Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers 2009.03394, arXiv.org, revised Jul 2021.
• Martin Beraja & David Y. Yang & Noam Yuchtman, 2020. "Data-intensive Innovation and the State: Evidence from AI Firms in China," NBER Working Papers 27723, National Bureau of Economic Research, Inc.