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 (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:

• 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, Center for Open Science, number vf28g, Dec, DOI: 10.31219/osf.io/vf28g.
• Taylan Kabbani & Fatih Enes Usta, 2022, "Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark," Papers, arXiv.org, number 2201.12283, Jan.
• Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, , "Estimation of the Farm-Level Yield-Weather-Relation Using Machine Learning," 61st Annual Conference, Berlin, Germany, September 22-24, 2021, German Association of Agricultural Economists (GEWISOLA), number 317075, DOI: 10.22004/ag.econ.317075.
• Ron Kaniel & Zihan Lin & Markus Pelger & Stijn Van Nieuwerburgh, 2022, "Machine-Learning the Skill of Mutual Fund Managers," NBER Working Papers, National Bureau of Economic Research, Inc, number 29723, Feb.
• Rahal, Charles & Verhagen, Mark D. & Kirk, David, 2021, "The Rise of Machine Learning in the Academic Social Sciences," SocArXiv, Center for Open Science, number gydve, Oct, DOI: 10.31219/osf.io/gydve.
• Koch, Bernard & Sainburg, Tim & Geraldo, Pablo & JIANG, SONG & Sun, Yizhou & Foster, Jacob G., 2021, "Deep Learning of Potential Outcomes," SocArXiv, Center for Open Science, number aeszf, Oct, DOI: 10.31219/osf.io/aeszf.
• Stephan Martin, 2022, "Estimation of Conditional Random Coefficient Models using Machine Learning Techniques," Papers, arXiv.org, number 2201.08366, Jan.
• Thackway, William & Ng, Matthew Kok Ming & Lee, Chyi Lin & Pettit, Christopher, 2021, "Building a predictive machine learning model of gentrification in Sydney," SocArXiv, Center for Open Science, number hkc96, Dec, DOI: 10.31219/osf.io/hkc96.
• Kresova, Svetlana & Hess, Sebastian, , "Determinants of Regional Raw Milk Prices in Russia," 61st Annual Conference, Berlin, Germany, September 22-24, 2021, German Association of Agricultural Economists (GEWISOLA), number 317051, DOI: 10.22004/ag.econ.317051.
• Verhagen, Mark D., 2021, "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv, Center for Open Science, number bka76, Dec, DOI: 10.31219/osf.io/bka76.
• Bruno Mazorra & Victor Adan & Vanesa Daza, 2022, "Do not rug on me: Zero-dimensional Scam Detection," Papers, arXiv.org, number 2201.07220, Jan.
• Emerson Melo, 2021, "Learning In Random Utility Models Via Online Decision Problems," CAEPR Working Papers, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington, number 2022-003 Classification-D, Aug.
• 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, arXiv.org, number 2201.11441, Jan.
• Item repec:cam:camjip:2104 is not listed on IDEAS anymore

• Item repec:rim:rimwps:22-04 is not listed on IDEAS anymore

• Körtner, John & Bonoli, Giuliano, 2021, "Predictive Algorithms in the Delivery of Public Employment Services," SocArXiv, Center for Open Science, number j7r8y, Dec, DOI: 10.31219/osf.io/j7r8y.
• 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, arXiv.org, number 2201.13267, Jan.
• Plantinga, Paul, 2022, "Digital discretion and public administration in Africa: Implications for the use of artificial intelligence," SocArXiv, Center for Open Science, number 2r98w, Jan, DOI: 10.31219/osf.io/2r98w.
• Oecd, 2022, "OECD Framework for the Classification of AI systems," OECD Digital Economy Papers, OECD Publishing, number 323, Feb, DOI: 10.1787/cb6d9eca-en.
• Rafael R. S. Guimaraes, 2022, "Deep Learning Macroeconomics," Papers, arXiv.org, number 2201.13380, Jan.
• Morteza Taiebat & Elham Amini & Ming Xu, 2022, "Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach," Papers, arXiv.org, number 2201.12696, Jan.
• Gabriel Okasa, 2022, "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers, arXiv.org, number 2201.12692, Jan.
• 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, arXiv.org, number 2201.12263, Jan, revised Jun 2023.
• Akey, Pat & Grégoire, Vincent & Martineau, Charles, 2021, "Price Revelation from Insider Trading: Evidence from Hacked Earnings News," SocArXiv, Center for Open Science, number qe6tu, Dec, DOI: 10.31219/osf.io/qe6tu.
• 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, Center for Open Science, number 8w7bg, Nov, DOI: 10.31219/osf.io/8w7bg.
• Jiayue Xu, 2022, "A hybrid deep learning approach for purchasing strategy of carbon emission rights -- Based on Shanghai pilot market," Papers, arXiv.org, number 2201.13235, Jan.
• 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, number hal-03448375, Dec.
• Christian A. Scholbeck & Giuseppe Casalicchio & Christoph Molnar & Bernd Bischl & Christian Heumann, 2022, "Marginal Effects for Non-Linear Prediction Functions," Papers, arXiv.org, number 2201.08837, Jan.
• 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, arXiv.org, number 2201.12286, Jan.
• Peng Li & Arim Park & Soohyun Cho & Yao Zhao, 2022, "Toward a More Populous Online Platform: The Economic Impacts of Compensated Reviews," Papers, arXiv.org, number 2201.11051, Jan, 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, arXiv.org, number 2201.12291, Jan.
• Beatrice Acciaio & Anastasis Kratsios & Gudmund Pammer, 2022, "Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer," Papers, arXiv.org, number 2201.13094, Jan, 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, arXiv.org, number 2201.09058, Dec, revised Aug 2022.

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 (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: