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 (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:

• Juan Manuel Dodero, 2021, "Artificial intelligence masters’ programmes - An analysis of curricula building blocks," JRC Research Reports, Joint Research Centre, number JRC123713, May.
• Christophe Schalck & Meryem Schalck, 2021, "Predicting French SME Failures: New Evidence from Machine Learning Techniques," Working Papers, Department of Research, Ipag Business School, number 2021-009, Jan.
• Zhang, Han, 2021, "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv, Center for Open Science, number 453jk, May, DOI: 10.31219/osf.io/453jk.
• Ming Min & Ruimeng Hu, 2021, "Signatured Deep Fictitious Play for Mean Field Games with Common Noise," Papers, arXiv.org, number 2106.03272, Jun.
• Item repec:rnp:wpaper:s21105 is not listed on IDEAS anymore

• Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021, "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers, arXiv.org, number 2105.15197, May, revised Oct 2022.
• Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2021, "Demand Estimation Using Managerial Responses to Automated Price Recommendations," CESifo Working Paper Series, CESifo, number 9127.
• Koya Ishikawa & Kazuhide Nakata, 2021, "Online Trading Models with Deep Reinforcement Learning in the Forex Market Considering Transaction Costs," Papers, arXiv.org, number 2106.03035, Jun, revised Dec 2021.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021, "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers, HAL, number halshs-03231786, May.
• 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, arXiv.org, number 2106.00647, Jun, revised Sep 2021.
• Hinterlang, Natascha & Hollmayr, Josef, 2021, "Classification of monetary and fiscal dominance regimes using machine learning techniques," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 160.
• 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, arXiv.org, number 2106.02522, Jun, revised Nov 2021.
• Kenta IKEUCHI, 2021, "Employment and Productivity Dynamics and Patent Applications Related to the Fourth Industrial Revolution (Japanese)," Discussion Papers (Japanese), Research Institute of Economy, Trade and Industry (RIETI), number 21011, Mar.
• Xavier Warin, 2021, "Reservoir optimization and Machine Learning methods," Papers, arXiv.org, number 2106.08097, Jun, revised May 2023.
• Daniel Hopp, 2021, "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Papers, arXiv.org, number 2106.08901, Jun.
• Lukas Gonon, 2021, "Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality," Papers, arXiv.org, number 2106.08900, Jun.
• Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021, "Deep Learning Statistical Arbitrage," Papers, arXiv.org, number 2106.04028, Jun, revised Oct 2022.
• Matus, Kira & Veale, Michael, 2021, "Certification Systems for Machine Learning: Lessons from Sustainability," SocArXiv, Center for Open Science, number pm3wy, Jun, DOI: 10.31219/osf.io/pm3wy.
• Ali Hirsa & Joerg Osterrieder & Branka Hadji-Misheva & Jan-Alexander Posth, 2021, "Deep reinforcement learning on a multi-asset environment for trading," Papers, arXiv.org, number 2106.08437, Jun.
• Kieran Wood & Stephen Roberts & Stefan Zohren, 2021, "Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection," Papers, arXiv.org, number 2105.13727, May, 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, Department of Research, Ipag Business School, number 2020-011, Jan.
• 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, arXiv.org, number 2105.14318, May.
• Hübler, Olaf, 2021, "Ungleich verteilte Corona-Infektionen zwischen den Bundesländern," Hannover Economic Papers (HEP), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, number dp-687, Jun.
• Oecd, 2021, "State of implementation of the OECD AI Principles: Insights from national AI policies," OECD Digital Economy Papers, OECD Publishing, number 311, Jun, DOI: 10.1787/1cd40c44-en.
• Item repec:rnp:wpaper:s21168 is not listed on IDEAS anymore

• Georges Casamatta & Sauveur Giannoni & Daniel Brunstein & Johan Jouve, 2021, "Host type and pricing on Airbnb: Seasonality and perceived market power," Working Papers, Laboratoire Lieux, Identités, eSpaces et Activités (LISA), number 021, May.
• Rokas Kaminskas & Modestas Stukas & Linas Jurksas, 2021, "ECB Communication: What Is It Telling Us?," Bank of Lithuania Discussion Paper Series, Bank of Lithuania, number 25, May.
• Peter Fisker & David Malmgren-Hansen & Thomas Pave Sohnesen, 2021, "Remote sensing of urban cyclone impact and resilience: Evidence from Idai," WIDER Working Paper Series, World Institute for Development Economic Research (UNU-WIDER), number wp-2021-89.
• Stefano Cabras & Marco Delogu & J.D. Tena, 2021, "Forced to Play Too Many Matches? A DeepLearning Assessment of Crowded Schedule," Working Papers, University of Liverpool, Department of Economics, number 202110 Classification-.

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 (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: