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 (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:

• 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, arXiv.org, number 2010.12002, Oct.
• Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020, "News media vs. FRED-MD for macroeconomic forecasting," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 08/2020, Oct.
• 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, Monash University, SoDa Laboratories, number 2020-02, Sep.
• 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, arXiv.org, number 2010.08463, Oct, 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, Center for Open Science, number haf2v, Oct, DOI: 10.31219/osf.io/haf2v.
• 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, Center for Open Science, number kczj5, Oct, DOI: 10.31219/osf.io/kczj5.
• 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, Center for Open Science, number 5dwrt, Oct, DOI: 10.31219/osf.io/5dwrt.
• 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, Center for Open Science, number 9vdwf, Oct, DOI: 10.31219/osf.io/9vdwf.
• 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, Center for Open Science, number auyvc, Oct, DOI: 10.31219/osf.io/auyvc.
• 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, Monash University, SoDa Laboratories, number 2020-04, Oct.
• Chipidza, Wallace & Yan, Jie, 2020, "Does Flagging POTUS’s Tweets Lead to Fewer or More Retweets? Preliminary Evidence from Machine Learning Models," SocArXiv, Center for Open Science, number 69hkb, Jul, DOI: 10.31219/osf.io/69hkb.
• 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, arXiv.org, number 2010.12038, Oct.
• Krotova, Alevtina & Mertens, Armin & Scheufen, Marc, 2020, "Open data and data sharing: An economic analysis," IW policy papers, Institut der deutschen Wirtschaft (IW) / German Economic Institute, number 21/2020.
• Roland Hodler & Michael Lechner & Paul A. Raschky, 2020, "Reassessing the Resource Curse using Causal Machine Learning," SoDa Laboratories Working Paper Series, Monash University, SoDa Laboratories, number 2020-01, Sep.
• João Amador & Tiago Alves, 2020, "Assessing the Scoreboard of the EU Macroeconomic Imbalances Procedure: (Machine) Learning from Decisions," Working Papers, Banco de Portugal, Economics and Research Department, number w202016.
• Dickinson, Jeffrey, 2020, "Planes, Trains, and Automobiles: What Drives Human-Made Light?," MPRA Paper, University Library of Munich, Germany, number 103504.
• Klaus Ackermann & Simon D. Angus & Paul A. Raschky, 2020, "Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)," Papers, arXiv.org, number 2010.08102, Oct.
• Fumihiko Isada, 0000, "Position in inter-organizational networks and profitability and growth potential," Proceedings of International Academic Conferences, International Institute of Social and Economic Sciences, number 11713169.
• Belloc, Filippo & Burdin, Gabriel & Landini, Fabio, 2020, "Robots and Worker Voice: An Empirical Exploration," IZA Discussion Papers, IZA Network @ LISER, number 13799, Oct.
• Tahir Miriyev & Alessandro Contu & Kevin Schafers & Ion Gabriel Ion, 2020, "Hybrid Modelling Approaches for Forecasting Energy Spot Prices in EPEC market," Papers, arXiv.org, number 2010.08400, Oct.
• Mike Ludkovski & Yuri Saporito, 2020, "KrigHedge: Gaussian Process Surrogates for Delta Hedging," Papers, arXiv.org, number 2010.08407, Oct, 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, IZA Network @ LISER, number 13776, Oct.
• Wilde, Joshua & Chen, Wei & Lohmann, Sophie, 2020, "COVID-19 and the Future of US Fertility: What Can We Learn from Google?," SocArXiv, Center for Open Science, number 2bgqs, Oct, DOI: 10.31219/osf.io/2bgqs.
• Gloria Gheno, 0000, "An new algorithm for citation analysis," Proceedings of International Academic Conferences, International Institute of Social and Economic Sciences, number 11113161.
• Xing Yan & Weizhong Zhang & Lin Ma & Wei Liu & Qi Wu, 2020, "Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning," Papers, arXiv.org, number 2010.08263, Oct.
• David Salvetat & Jean-Sébastien Lacam, 2020, "Data determinants of the activity of SMEs automobile dealers," Post-Print, HAL, number hal-02965540, Oct.
• Huwe, Vera & Gessner, Johannes, 2020, "Are there rebound effects from electric vehicle adoption? Evidence from German household data," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 20-048.
• Müller, Karsten, 2020, "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin, number 23, DOI: 10.18452/22014.

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 (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: