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 (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:

• Marshall Burke & Anne Driscoll & David Lobell & Stefano Ermon, 2020, "Using Satellite Imagery to Understand and Promote Sustainable Development," NBER Working Papers, National Bureau of Economic Research, Inc, number 27879, Oct.
• 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, arXiv.org, number 2010.01265, Oct, revised Jan 2021.
• Lorenc Kapllani & Long Teng, 2020, "Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations," Papers, arXiv.org, number 2010.01319, Oct, revised Jun 2022.
• Jillian Grennan & Roni Michaely, 2020, "Artificial Intelligence and High-Skilled Work: Evidence from Analysts," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-84, Aug.
• Hinterlang, Natascha & Hollmayr, Josef, 2020, "Classification of monetary and fiscal dominance regimes using machine learning techniques," Discussion Papers, Deutsche Bundesbank, number 51/2020.
• Honorata Bogusz & Szymon Winnicki & Piotr Wójcik, 2020, "What factors determine unequal suburbanisation? New evidence from Warsaw, Poland," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-34.
• Tullio Mancini & Hector Calvo-Pardo & Jose Olmo, 2020, "Prediction intervals for Deep Neural Networks," Papers, arXiv.org, number 2010.04044, Oct, revised May 2021.
• Boeing, Geoff, 2020, "Street Network Models and Indicators for Every Urban Area in the World," SocArXiv, Center for Open Science, number f2dqc, Sep, DOI: 10.31219/osf.io/f2dqc.
• Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020, "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers, arXiv.org, number 2009.13384, Sep.
• Janusz Gajda & Rafał Walasek, 2020, "Fractional differentiation and its use in machine learning," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-32.
• 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, Centre for Health Economics, University of York, number 173cherp, Oct.
• Breithaupt, Patrick & Kesler, Reinhold & Niebel, Thomas & Rammer, Christian, 2020, "Intangible capital indicators based on web scraping of social media," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 20-046.
• Tarun Bhatia, 2020, "Predicting Non Farm Employment," Papers, arXiv.org, number 2009.14282, Sep.
• Lining Yu & Wolfgang Karl Hardle & Lukas Borke & Thijs Benschop, 2020, "An AI approach to measuring financial risk," Papers, arXiv.org, number 2009.13222, Sep.
• 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, ULB -- Universite Libre de Bruxelles, number 2013/312976, Sep.
• 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, National Bureau of Economic Research, Inc, number 27867, Sep.
• 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, number hal-02945775, Sep.
• 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, Center for Open Science, number jrc58, Sep, DOI: 10.31219/osf.io/jrc58.
• Nadja Klein & Michael Stanley Smith & David J. Nott, 2020, "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers, arXiv.org, number 2010.01844, Oct, revised May 2021.
• Masahiro Kato & Shota Yasui, 2020, "Learning Classifiers under Delayed Feedback with a Time Window Assumption," Papers, arXiv.org, number 2009.13092, Sep, revised Jun 2022.
• Rakshit Jha & Mattijs De Paepe & Samuel Holt & James West & Shaun Ng, 2020, "Deep Learning for Digital Asset Limit Order Books," Papers, arXiv.org, number 2010.01241, Oct.
• Abramov, Dimitri Marques, 2020, "A Complex System Needs Homeostasis: Market Self-Organization Through Negative Feedback Using A Floating Taxation Policy," SocArXiv, Center for Open Science, number xj2gb, Sep, DOI: 10.31219/osf.io/xj2gb.
• Susana Martínez-Restrepo & Lina Tafur Mar�n & Juan Guillermo Osio & Pablo Cort�s, 2020, "Violencias basadas en género en tiempos de Covid-19," Informes de Investigación, Fedesarrollo, number 18440, Sep.
• Marica Valente, 2020, "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Papers, arXiv.org, number 2010.01105, Oct, 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, IZA Network @ LISER, number 13767, Oct.
• Lopez, Claude & Contreras, Oscar & Bendix, Joseph, 2020, "Disagreement among ESG rating agencies: shall we be worried?," MPRA Paper, University Library of Munich, Germany, number 103027, Sep.

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 (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: