Report NEP-BIG-2020-11-02

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:

• Metod Jazbec & Barna P'asztor & Felix Faltings & Nino Antulov-Fantulin & Petter N. Kolm, 2020. "On the impact of publicly available news and information transfer to financial markets," Papers 2010.12002, arXiv.org.
• Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Papers No 08/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
• Klaus Ackermann & Alexey Chernikov & Nandini Anantharama & Miethy Zaman & Paul A Raschky, 2020. "Object Recognition for Economic Development from Daytime Satellite Imagery," SoDa Laboratories Working Paper Series 2020-02, Monash University, SoDa Laboratories.
• Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness," Papers 2010.08463, arXiv.org, revised Nov 2025.
• Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
• Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawRxiv kczj5, Center for Open Science.
• Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
• Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
• Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
• Klaus Ackermann & Simon D Angus & Paul A Raschky, 2020. "Estimating Sleep and Work Hours from Alternative Data by Segmented Functional Classification Analysis, SFCA," SoDa Laboratories Working Paper Series 2020-04, Monash University, SoDa Laboratories.
• Chipidza, Wallace & Yan, Jie, 2020. "Does Flagging POTUS’s Tweets Lead to Fewer or More Retweets? Preliminary Evidence from Machine Learning Models," SocArXiv 69hkb, Center for Open Science.
• Miguel Baritto & Md Mashum Billal & S. M. Muntasir Nasim & Rumana Afroz Sultana & Mohammad Arani & Ahmed Jawad Qureshi, 2020. "Supporting Tool for The Transition of Existing Small and Medium Enterprises Towards Industry 4.0," Papers 2010.12038, arXiv.org.
• Krotova, Alevtina & Mertens, Armin & Scheufen, Marc, 2020. "Open data and data sharing: An economic analysis," IW policy papers 21/2020, Institut der deutschen Wirtschaft (IW) / German Economic Institute.
• Roland Hodler & Michael Lechner & Paul A. Raschky, 2020. "Reassessing the Resource Curse using Causal Machine Learning," SoDa Laboratories Working Paper Series 2020-01, Monash University, SoDa Laboratories.
• João Amador & Tiago Alves, 2020. "Assessing the Scoreboard of the EU Macroeconomic Imbalances Procedure: (Machine) Learning from Decisions," Working Papers w202016, Banco de Portugal, Economics and Research Department.
• Dickinson, Jeffrey, 2020. "Planes, Trains, and Automobiles: What Drives Human-Made Light?," MPRA Paper 103504, University Library of Munich, Germany.
• Klaus Ackermann & Simon D. Angus & Paul A. Raschky, 2020. "Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)," Papers 2010.08102, arXiv.org.
• Fumihiko Isada, 0000. "Position in inter-organizational networks and profitability and growth potential," Proceedings of International Academic Conferences 11713169, International Institute of Social and Economic Sciences.
• Belloc, Filippo & Burdin, Gabriel & Landini, Fabio, 2020. "Robots and Worker Voice: An Empirical Exploration," IZA Discussion Papers 13799, Institute of Labor Economics (IZA).
• Tahir Miriyev & Alessandro Contu & Kevin Schafers & Ion Gabriel Ion, 2020. "Hybrid Modelling Approaches for Forecasting Energy Spot Prices in EPEC market," Papers 2010.08400, arXiv.org.
• Mike Ludkovski & Yuri Saporito, 2020. "KrigHedge: Gaussian Process Surrogates for Delta Hedging," Papers 2010.08407, arXiv.org, revised Jan 2022.
• Wilde, Joshua & Chen, Wei & Lohmann, Sophie, 2020. "COVID-19 and the Future of US Fertility: What Can We Learn from Google?," IZA Discussion Papers 13776, Institute of Labor Economics (IZA).
• Wilde, Joshua & Chen, Wei & Lohmann, Sophie, 2020. "COVID-19 and the Future of US Fertility: What Can We Learn from Google?," SocArXiv 2bgqs, Center for Open Science.
• Gloria Gheno, 0000. "An new algorithm for citation analysis," Proceedings of International Academic Conferences 11113161, International Institute of Social and Economic Sciences.
• Xing Yan & Weizhong Zhang & Lin Ma & Wei Liu & Qi Wu, 2020. "Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning," Papers 2010.08263, arXiv.org.
• David Salvetat & Jean-Sébastien Lacam, 2020. "Data determinants of the activity of SMEs automobile dealers," Post-Print hal-02965540, HAL.
• Huwe, Vera & Gessner, Johannes, 2020. "Are there rebound effects from electric vehicle adoption? Evidence from German household data," ZEW Discussion Papers 20-048, ZEW - Leibniz Centre for European Economic Research.
• Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.