Report NEP-BIG-2020-10-19
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.Subscribe to this report: email, RSS, or Mastodon.
Other reports in NEP-BIG
The following items were announced in this report:
- Marshall Burke & Anne Driscoll & David Lobell & Stefano Ermon, 2020. "Using Satellite Imagery to Understand and Promote Sustainable Development," NBER Working Papers 27879, National Bureau of Economic Research, Inc.
- Chuheng Zhang & Yuanqi Li & Xi Chen & Yifei Jin & Pingzhong Tang & Jian Li, 2020. "DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis," Papers 2010.01265, arXiv.org, revised Jan 2021.
- Lorenc Kapllani & Long Teng, 2020. "Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations," Papers 2010.01319, arXiv.org, revised Jun 2022.
- Jillian Grennan & Roni Michaely, 2020. "Artificial Intelligence and High-Skilled Work: Evidence from Analysts," Swiss Finance Institute Research Paper Series 20-84, Swiss Finance Institute.
- Hinterlang, Natascha & Hollmayr, Josef, 2020. "Classification of monetary and fiscal dominance regimes using machine learning techniques," Discussion Papers 51/2020, Deutsche Bundesbank.
- Honorata Bogusz & Szymon Winnicki & Piotr Wójcik, 2020. "What factors determine unequal suburbanisation? New evidence from Warsaw, Poland," Working Papers 2020-34, Faculty of Economic Sciences, University of Warsaw.
- Tullio Mancini & Hector Calvo-Pardo & Jose Olmo, 2020. "Prediction intervals for Deep Neural Networks," Papers 2010.04044, arXiv.org, revised May 2021.
- Boeing, Geoff, 2020. "Street Network Models and Indicators for Every Urban Area in the World," SocArXiv f2dqc, Center for Open Science.
- Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020. "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers 2009.13384, arXiv.org.
- Janusz Gajda & Rafał Walasek, 2020. "Fractional differentiation and its use in machine learning," Working Papers 2020-32, Faculty of Economic Sciences, University of Warsaw.
- Noemi Kreif & Andrew Mirelman & Rodrigo Moreno-Serra & Taufik Hidayat, & Karla DiazOrdaz & Marc Suhrcke, 2020. "Who benefits from health insurance? Uncovering heterogeneous policy impacts using causal machine learning," Working Papers 173cherp, Centre for Health Economics, University of York.
- Breithaupt, Patrick & Kesler, Reinhold & Niebel, Thomas & Rammer, Christian, 2020. "Intangible capital indicators based on web scraping of social media," ZEW Discussion Papers 20-046, ZEW - Leibniz Centre for European Economic Research.
- Tarun Bhatia, 2020. "Predicting Non Farm Employment," Papers 2009.14282, arXiv.org.
- Lining Yu & Wolfgang Karl Hardle & Lukas Borke & Thijs Benschop, 2020. "An AI approach to measuring financial risk," Papers 2009.13222, arXiv.org.
- Stefanos Georganos & Oscar Brousse & Sébastien Dujardin & Catherine Linard & Daniel Casey & Marco Milliones & Benoit Parmentier & Nicole P M Van Lipzig & Matthias Demuzere & Taïs Grippa & Sabine Vanhu, 2020. "Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators," ULB Institutional Repository 2013/312976, ULB -- Universite Libre de Bruxelles.
- Steven J. Davis & Stephen Hansen & Cristhian Seminario-Amez, 2020. "Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19," NBER Working Papers 27867, National Bureau of Economic Research, Inc.
- Rotem Zelingher & David Makowski & Thierry Brunelle, 2020. "Forecasting impacts of Agricultural Production on Global Maize Price [Prévision des impacts de la production agricole sur les prix mondiaux du maïs]," CIRED Working Papers hal-02945775, HAL.
- Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
- Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
- Masahiro Kato & Shota Yasui, 2020. "Learning Classifiers under Delayed Feedback with a Time Window Assumption," Papers 2009.13092, arXiv.org, revised Jun 2022.
- Rakshit Jha & Mattijs De Paepe & Samuel Holt & James West & Shaun Ng, 2020. "Deep Learning for Digital Asset Limit Order Books," Papers 2010.01241, arXiv.org.
- Abramov, Dimitri Marques, 2020. "A Complex System Needs Homeostasis: Market Self-Organization Through Negative Feedback Using A Floating Taxation Policy," SocArXiv xj2gb, Center for Open Science.
- Susana Martínez-Restrepo & Lina Tafur Marín & Juan Guillermo Osio & Pablo Cortés, 2020. "Violencias basadas en género en tiempos de Covid-19," Informes de Investigación 18440, Fedesarrollo.
- Marica Valente, 2020. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Papers 2010.01105, arXiv.org, revised Nov 2022.
- Schmid, Christian P. R. & Schreiner, Nicolas & Stutzer, Alois, 2020. "Transfer Payment Systems and Financial Distress: Insights from Health Insurance Premium Subsidies," IZA Discussion Papers 13767, Institute of Labor Economics (IZA).
- Lopez, Claude & Contreras, Oscar & Bendix, Joseph, 2020. "Disagreement among ESG rating agencies: shall we be worried?," MPRA Paper 103027, University Library of Munich, Germany.