Report NEP-BIG-2021-05-03

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:

• Luna Yue Huang & Solomon Hsiang & Marco Gonzalez-Navarro, 2021. "Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs," Papers 2104.11772, arXiv.org.
• Calypso Herrera & Florian Krach & Pierre Ruyssen & Josef Teichmann, 2021. "Optimal Stopping via Randomized Neural Networks," Papers 2104.13669, arXiv.org, revised Dec 2023.
• Ekaterina Zolotareva, 2021. "Applying Convolutional Neural Networks for Stock Market Trends Identification," Papers 2104.13948, arXiv.org.
• Yusuke Narita & Kohei Yata, 2021. "Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Papers 2104.12909, arXiv.org, revised Dec 2023.
• Luigi Biagini & Simone Severini, 2021. "The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool," Papers 2104.14188, arXiv.org.
• Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Papers 2104.14286, arXiv.org.
• April Wu & Paul O'Leary & Denise Hoffman, 2021. "Trends in Opioid Use among Social Security Disability Insurance Applicants," Working Papers, Center for Retirement Research at Boston College 2021-06, Center for Retirement Research.
• Ramis Khbaibullin & Sergei Seleznev, 2020. "Stochastic Gradient Variational Bayes and Normalizing Flows for Estimating Macroeconomic Models," Bank of Russia Working Paper Series wps61, Bank of Russia.
• Sarracino, Francesco & Greyling, Talita & O'Connor , Kelsey & Peroni, Chiara & Rossouw, Stephanie, 2021. "A year of pandemic: levels, changes and validity of well-being data from Twitter. Evidence from ten countries," GLO Discussion Paper Series 831, Global Labor Organization (GLO).
• Jacob, Daniel, 2021. "CATE meets ML: Conditional average treatment effect and machine learning," IRTG 1792 Discussion Papers 2021-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klockl, 2021. "Computational Performance of Deep Reinforcement Learning to find Nash Equilibria," Papers 2104.12895, arXiv.org.
• Dylan Brewer & Alyssa Carlson, 2021. "Addressing Sample Selection Bias for Machine Learning Methods," Working Papers 2102, Department of Economics, University of Missouri.
• Bauer, Kevin & Gill, Andrej, 2021. "Mirror, mirror on the wall: Machine predictions and self-fulfilling prophecies," SAFE Working Paper Series 313, Leibniz Institute for Financial Research SAFE.
• Xin Zhang & Lan Wu & Zhixue Chen, 2021. "Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm," Papers 2104.12484, arXiv.org.
• Shohei Nakazato & Mariagrazia Squicciarini, 2021. "Artificial intelligence companies, goods and services: A trademark-based analysis," OECD Science, Technology and Industry Working Papers 2021/06, OECD Publishing.
• Rammer, Christian & Fernández, Gastón P. & Czarnitzki, Dirk, 2021. "Artificial intelligence and industrial innovation: Evidence from firm-level data," ZEW Discussion Papers 21-036, ZEW - Leibniz Centre for European Economic Research.
• Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
• David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin & Bonsoo Koo, 2021. "Loss-Based Variational Bayes Prediction," Papers 2104.14054, arXiv.org, revised May 2022.
• Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2021. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," BEMPS - Bozen Economics & Management Paper Series BEMPS83, Faculty of Economics and Management at the Free University of Bozen.
• Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & François Chareyron, 2021. "Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models," Working Papers hal-03202431, HAL.