Report NEP-BIG-2022-03-07

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:

• Daoud, Adel & Jordan, Felipe & Sharma, Makkunda & Johansson, Fredrik & Dubhashi, Devdatt & Paul, Sourabh & Banerjee, Subhashis, 2021. "Using satellites and artificial intelligence to measure health and material-living standards in India," SocArXiv vf28g, Center for Open Science.
• Taylan Kabbani & Fatih Enes Usta, 2022. "Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark," Papers 2201.12283, arXiv.org.
• Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2021. "Estimation of the Farm-Level Yield-Weather-Relation Using Machine Learning," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317075, German Association of Agricultural Economists (GEWISOLA).
• Ron Kaniel & Zihan Lin & Markus Pelger & Stijn Van Nieuwerburgh, 2022. "Machine-Learning the Skill of Mutual Fund Managers," NBER Working Papers 29723, National Bureau of Economic Research, Inc.
• Rahal, Charles & Verhagen, Mark D. & Kirk, David, 2021. "The Rise of Machine Learning in the Academic Social Sciences," SocArXiv gydve, Center for Open Science.
• Koch, Bernard & Sainburg, Tim & Geraldo, Pablo & JIANG, SONG & Sun, Yizhou & Foster, Jacob G., 2021. "Deep Learning of Potential Outcomes," SocArXiv aeszf, Center for Open Science.
• Stephan Martin, 2022. "Estimation of Conditional Random Coefficient Models using Machine Learning Techniques," Papers 2201.08366, arXiv.org.
• Thackway, William & Ng, Matthew Kok Ming & Lee, Chyi Lin & Pettit, Christopher, 2021. "Building a predictive machine learning model of gentrification in Sydney," SocArXiv hkc96, Center for Open Science.
• Kresova, Svetlana & Hess, Sebastian, 2021. "Determinants of Regional Raw Milk Prices in Russia," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317051, German Association of Agricultural Economists (GEWISOLA).
• Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
• Bruno Mazorra & Victor Adan & Vanesa Daza, 2022. "Do not rug on me: Zero-dimensional Scam Detection," Papers 2201.07220, arXiv.org.
• Emerson Melo, 2021. "Learning In Random Utility Models Via Online Decision Problems," CAEPR Working Papers 2022-003 Classification-D, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
• Raphael Koster & Jan Balaguer & Andrea Tacchetti & Ari Weinstein & Tina Zhu & Oliver Hauser & Duncan Williams & Lucy Campbell-Gillingham & Phoebe Thacker & Matthew Botvinick & Christopher Summerfield, 2022. "Human-centered mechanism design with Democratic AI," Papers 2201.11441, arXiv.org.
• Charlson, G., 2021. "Third-Degree Price Discrimination in the Age of Big Data," Janeway Institute Working Papers 2104, Faculty of Economics, University of Cambridge.
• Daniel Levy & Tamir Mayer & Alon Raviv, 2022. "Economists in the 2008 Financial Crisis: Slow to See, Fast to Act," Working Paper series 22-04, Rimini Centre for Economic Analysis.
• Körtner, John & Bonoli, Giuliano, 2021. "Predictive Algorithms in the Delivery of Public Employment Services," SocArXiv j7r8y, Center for Open Science.
• Ihsan Chaoubi & Camille Besse & H'el`ene Cossette & Marie-Pier C^ot'e, 2022. "Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network," Papers 2201.13267, arXiv.org.
• Plantinga, Paul, 2022. "Digital discretion and public administration in Africa: Implications for the use of artificial intelligence," SocArXiv 2r98w, Center for Open Science.
• Oecd, 2022. "OECD Framework for the Classification of AI systems," OECD Digital Economy Papers 323, OECD Publishing.
• Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
• Morteza Taiebat & Elham Amini & Ming Xu, 2022. "Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach," Papers 2201.12696, arXiv.org.
• Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
• Krzysztof Rusek & Piotr Bory{l}o & Piotr Jaglarz & Fabien Geyer & Albert Cabellos & Piotr Cho{l}da, 2022. "RiskNet: Neural Risk Assessment in Networks of Unreliable Resources," Papers 2201.12263, arXiv.org, revised Jun 2023.
• Akey, Pat & Grégoire, Vincent & Martineau, Charles, 2021. "Price Revelation from Insider Trading: Evidence from Hacked Earnings News," SocArXiv qe6tu, Center for Open Science.
• Beck, Ben & Winters, Meghan & Nelson, Trisalyn & Pettit, Christopher & Saberi, Meead & Thompson, Jason & Seneviratne, Sachith & Nice, Kerry A & Zarpelon-Leao, Simone & Stevenson, Mark, 2021. "Developing urban biking typologies: quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics," SocArXiv 8w7bg, Center for Open Science.
• Jiayue Xu, 2022. "A hybrid deep learning approach for purchasing strategy of carbon emission rights -- Based on Shanghai pilot market," Papers 2201.13235, arXiv.org.
• Sébastien Marchand & Damien Cubizol & Elda Nasho Ah-Pine & Huanxiu Guo, 2023. "Policy change, mass media and air quality in China: new paths to face air pollution?," CERDI Working papers hal-03448375, HAL.
• Christian A. Scholbeck & Giuseppe Casalicchio & Christoph Molnar & Bernd Bischl & Christian Heumann, 2022. "Marginal Effects for Non-Linear Prediction Functions," Papers 2201.08837, arXiv.org.
• Ivan Letteri & Giuseppe Della Penna & Giovanni De Gasperis & Abeer Dyoub, 2022. "A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron," Papers 2201.12286, arXiv.org.
• Peng Li & Arim Park & Soohyun Cho & Yao Zhao, 2022. "Toward a More Populous Online Platform: The Economic Impacts of Compensated Reviews," Papers 2201.11051, arXiv.org, revised Oct 2024.
• Effat Ara Easmin Lucky & Md. Mahadi Hasan Sany & Mumenunnesa Keya & Md. Moshiur Rahaman & Umme Habiba Happy & Sharun Akter Khushbu & Md. Arid Hasan, 2022. "Simulating Using Deep Learning The World Trade Forecasting of Export-Import Exchange Rate Convergence Factor During COVID-19," Papers 2201.12291, arXiv.org.
• Beatrice Acciaio & Anastasis Kratsios & Gudmund Pammer, 2022. "Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer," Papers 2201.13094, arXiv.org, revised Mar 2023.
• Shuo Sun & Wanqi Xue & Rundong Wang & Xu He & Junlei Zhu & Jian Li & Bo An, 2021. "DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities," Papers 2201.09058, arXiv.org, revised Aug 2022.