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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 (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:
- Luna Yue Huang & Solomon Hsiang & Marco Gonzalez-Navarro, 2021, "Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs," Papers, arXiv.org, number 2104.11772, Apr.
- Calypso Herrera & Florian Krach & Pierre Ruyssen & Josef Teichmann, 2021, "Optimal Stopping via Randomized Neural Networks," Papers, arXiv.org, number 2104.13669, Apr, revised Dec 2023.
- Ekaterina Zolotareva, 2021, "Applying Convolutional Neural Networks for Stock Market Trends Identification," Papers, arXiv.org, number 2104.13948, Apr.
- Yusuke Narita & Kohei Yata, 2021, "Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Papers, arXiv.org, number 2104.12909, Apr, 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, arXiv.org, number 2104.14188, Apr.
- 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, arXiv.org, number 2104.14286, Apr.
- 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, Center for Retirement Research, number 2021-06, Mar.
- Ramis Khbaibullin & Sergei Seleznev, 2020, "Stochastic Gradient Variational Bayes and Normalizing Flows for Estimating Macroeconomic Models," Bank of Russia Working Paper Series, Bank of Russia, number wps61, Oct.
- 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, Global Labor Organization (GLO), number 831.
- Jacob, Daniel, 2021, "CATE meets ML: Conditional average treatment effect and machine learning," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2021-005.
- Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klockl, 2021, "Computational Performance of Deep Reinforcement Learning to find Nash Equilibria," Papers, arXiv.org, number 2104.12895, Apr.
- Dylan Brewer & Alyssa Carlson, 2021, "Addressing Sample Selection Bias for Machine Learning Methods," Working Papers, Department of Economics, University of Missouri, number 2102.
- Bauer, Kevin & Gill, Andrej, 2021, "Mirror, mirror on the wall: Machine predictions and self-fulfilling prophecies," SAFE Working Paper Series, Leibniz Institute for Financial Research SAFE, number 313.
- Xin Zhang & Lan Wu & Zhixue Chen, 2021, "Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm," Papers, arXiv.org, number 2104.12484, Apr.
- Shohei Nakazato & Mariagrazia Squicciarini, 2021, "Artificial intelligence companies, goods and services: A trademark-based analysis," OECD Science, Technology and Industry Working Papers, OECD Publishing, number 2021/06, May, DOI: 10.1787/2db2d7f4-en.
- Rammer, Christian & Fernández, Gastón P. & Czarnitzki, Dirk, 2021, "Artificial intelligence and industrial innovation: Evidence from firm-level data," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 21-036.
- Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021, "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series, CESifo, number 9037.
- David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin & Bonsoo Koo, 2021, "Loss-Based Variational Bayes Prediction," Papers, arXiv.org, number 2104.14054, Apr, 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, Faculty of Economics and Management at the Free University of Bozen, number BEMPS83, Apr.
- 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, number hal-03202431, Apr.