Report NEP-CMP-2021-06-21

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-CMP

The following items were announced in this report:

• Ming Min & Ruimeng Hu, 2021, "Signatured Deep Fictitious Play for Mean Field Games with Common Noise," Papers, arXiv.org, number 2106.03272, Jun.
• Xavier Warin, 2021, "Reservoir optimization and Machine Learning methods," Papers, arXiv.org, number 2106.08097, Jun, revised May 2023.
• 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.
• 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.
• 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.
• 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.
• Daniel Hopp, 2021, "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Papers, arXiv.org, number 2106.08901, Jun.
• 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.
• 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.
• 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.
• 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.
• Lukas Gonon, 2021, "Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality," Papers, arXiv.org, number 2106.08900, Jun.
• Juan Manuel Dodero, 2021, "Artificial intelligence masters’ programmes - An analysis of curricula building blocks," JRC Research Reports, Joint Research Centre, number JRC123713, May.
• Nikolay Ivanov & Qiben Yan, 2021, "Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization," Papers, arXiv.org, number 2105.14194, May.
• Jaller, Miguel & Rodier, Caroline & Zhang, Michael & Lin, Huachao & Lewis, Kathryn, 2021, "Fighting for Curb Space: Parking, Ride-Hailing, Urban Freight Deliveries, and Other Users," Institute of Transportation Studies, Working Paper Series, Institute of Transportation Studies, UC Davis, number qt3jn371hw, Jun.
• Paul de Guibert & Behrang Shirizadeh & Philippe Quirion, 2020, "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Post-Print, HAL, number hal-03100309, Dec, DOI: 10.1016/j.energy.2020.119024.
• 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.
• Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021, "Deep Learning Statistical Arbitrage," Papers, arXiv.org, number 2106.04028, Jun, revised Oct 2022.
• 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.
• Zhengqing Zhou & Guanyang Wang & Jose Blanchet & Peter W. Glynn, 2021, "Unbiased Optimal Stopping via the MUSE," Papers, arXiv.org, number 2106.02263, Jun, revised Dec 2022.
• Item repec:rnp:wpaper:s21105 is not listed on IDEAS anymore

• Kenneth W. Clements & Marc Jim M. Mariano & George Verikios, 2021, "Expenditure Patterns, Heterogeneity And Long-Term Structural Change," Economics Discussion / Working Papers, The University of Western Australia, Department of Economics, number 21-10.
• 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.
• Liu Ziyin & Kentaro Minami & Kentaro Imajo, 2021, "Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction," Papers, arXiv.org, number 2106.04114, Jun, revised Dec 2022.
• Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2021, "Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling," Papers, arXiv.org, number 2106.03156, Jun, revised Oct 2021.
• Mathieu Mercadier & Jean-Pierre Lardy, 2019, "Credit spread approximation and improvement using random forest regression," Post-Print, HAL, number hal-03241566, Aug, DOI: 10.1016/j.ejor.2019.02.005.
• Guopeng Song & Roel Leus, 2021, "Parallel machine scheduling under uncertainty: The battle for robustness," Working Papers of Department of Decision Sciences and Information Management, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven, number 675543.
• Giovanni Dosi & Francesco Lamperti & Mariana Mazzucato & Mauro Napoletano & Andrea Roventini, 2021, "Mission-Oriented Policies and the "Entrepreneurial State" at Work: An Agent-Based Exploration," GREDEG Working Papers, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France, number 2021-25, Jun.

Report NEP-CMP-2021-06-21

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-CMP

The following items were announced in this report: