Report NEP-BIG-2021-06-21

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:

• Juan Manuel Dodero, 2021. "Artificial intelligence masters’ programmes - An analysis of curricula building blocks," JRC Research Reports JRC123713, Joint Research Centre.
• Christophe Schalck & Meryem Schalck, 2021. "Predicting French SME Failures: New Evidence from Machine Learning Techniques," Working Papers 2021-009, Department of Research, Ipag Business School.
• Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
• Ming Min & Ruimeng Hu, 2021. "Signatured Deep Fictitious Play for Mean Field Games with Common Noise," Papers 2106.03272, arXiv.org.
• Kaukin Andrey & Kosarev Vladimir, 2021. "Modeling and forecasting production indices using artificial neural networks, taking into account intersectoral relationships and comparing the predictive qualities of various architectures [Модели," Working Papers s21105, Russian Presidential Academy of National Economy and Public Administration.
• Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers 2105.15197, arXiv.org, revised Oct 2022.
• Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2021. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," CESifo Working Paper Series 9127, CESifo.
• Koya Ishikawa & Kazuhide Nakata, 2021. "Online Trading Models with Deep Reinforcement Learning in the Forex Market Considering Transaction Costs," Papers 2106.03035, arXiv.org, revised Dec 2021.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
• Matthieu Nadini & Laura Alessandretti & Flavio Di Giacinto & Mauro Martino & Luca Maria Aiello & Andrea Baronchelli, 2021. "Mapping the NFT revolution: market trends, trade networks and visual features," Papers 2106.00647, arXiv.org, revised Sep 2021.
• Hinterlang, Natascha & Hollmayr, Josef, 2021. "Classification of monetary and fiscal dominance regimes using machine learning techniques," IMFS Working Paper Series 160, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
• Junran Wu & Ke Xu & Xueyuan Chen & Shangzhe Li & Jichang Zhao, 2021. "Price graphs: Utilizing the structural information of financial time series for stock prediction," Papers 2106.02522, arXiv.org, revised Nov 2021.
• IKEUCHI Kenta, 2021. "Employment and Productivity Dynamics and Patent Applications Related to the Fourth Industrial Revolution (Japanese)," Discussion Papers (Japanese) 21011, Research Institute of Economy, Trade and Industry (RIETI).
• Xavier Warin, 2021. "Reservoir optimization and Machine Learning methods," Papers 2106.08097, arXiv.org, revised May 2023.
• Daniel Hopp, 2021. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Papers 2106.08901, arXiv.org.
• Lukas Gonon, 2021. "Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality," Papers 2106.08900, arXiv.org.
• Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
• Matus, Kira & Veale, Michael, 2021. "Certification Systems for Machine Learning: Lessons from Sustainability," SocArXiv pm3wy, Center for Open Science.
• Ali Hirsa & Joerg Osterrieder & Branka Hadji-Misheva & Jan-Alexander Posth, 2021. "Deep reinforcement learning on a multi-asset environment for trading," Papers 2106.08437, arXiv.org.
• Kieran Wood & Stephen Roberts & Stefan Zohren, 2021. "Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection," Papers 2105.13727, arXiv.org, revised Dec 2021.
• William Nganga Irungu & Julien Chevallier & Simon Wagura Ndiritu, 2020. "Regime Changes and Fiscal Sustainability in Kenya with Comparative Nonlinear Granger Causalities Across East-African Countries," Working Papers 2020-011, Department of Research, Ipag Business School.
• Da Zhang & Qingyi Wang & Shaojie Song & Simiao Chen & Mingwei Li & Lu Shen & Siqi Zheng & Bofeng Cai & Shenhao Wang, 2021. "Estimating air quality co-benefits of energy transition using machine learning," Papers 2105.14318, arXiv.org.
• Hübler, Olaf, 2021. "Ungleich verteilte Corona-Infektionen zwischen den Bundesländern," Hannover Economic Papers (HEP) dp-687, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
• Oecd, 2021. "State of implementation of the OECD AI Principles: Insights from national AI policies," OECD Digital Economy Papers 311, OECD Publishing.
• Trusov Alexandr & Botvich Dmitry & Maruev Sergey, 2021. "Night lights in determining and assessing socio-economic processes [Ночные огни при определениии оценке социально-эконoмических процессов]," Working Papers s21168, Russian Presidential Academy of National Economy and Public Administration.
• Georges Casamatta & Sauveur Giannoni & Daniel Brunstein & Johan Jouve, 2021. "Host type and pricing on Airbnb: Seasonality and perceived market power," Working Papers 021, Laboratoire Lieux, Identités, eSpaces et Activités (LISA).
• Rokas Kaminskas & Modestas Stukas & Linas Jurksas, 2021. "ECB Communication: What Is It Telling Us?," Bank of Lithuania Discussion Paper Series 25, Bank of Lithuania.
• Peter Fisker & David Malmgren-Hansen & Thomas Pave Sohnesen, 2021. "Remote sensing of urban cyclone impact and resilience: Evidence from Idai," WIDER Working Paper Series wp-2021-89, World Institute for Development Economic Research (UNU-WIDER).
• Stefano Cabras & Marco Delogu & J.D. Tena, 2021. "Forced to Play Too Many Matches? A DeepLearning Assessment of Crowded Schedule," Working Papers 202110 Classification-, University of Liverpool, Department of Economics.