Report NEP-CMP-2021-05-10

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

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Other reports in NEP-CMP

The following items were announced in this report:

• Riccardo Aiolfi & Nicola Moreni & Marco Bianchetti & Marco Scaringi & Filippo Fogliani, 2021. "Learning Bermudans," Papers 2105.00655, arXiv.org.
• David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
• Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
• Jayachandran, Seema & Biradavolu, Monica & Cooper, Jan, 2021. "Using machine learning and qualitative interviews to design a five-question women's agency index," CEPR Discussion Papers 15961, C.E.P.R. Discussion Papers.
• Navid Mottaghi & Sara Farhangdoost, 2021. "Stock Price Forecasting in Presence of Covid-19 Pandemic and Evaluating Performances of Machine Learning Models for Time-Series Forecasting," Papers 2105.02785, arXiv.org.
• Martin Huber & Jonas Meier & Hannes Wallimann, 2021. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Papers 2105.01426, arXiv.org, revised Jun 2022.
• Gorodnichenko, Yuriy & Maliar, Serguei & Naubert, Christopher, 2020. "Household Savings and Monetary Policy under Individual and Aggregate Stochastic Volatility," CEPR Discussion Papers 15614, C.E.P.R. Discussion Papers.
• Gambacorta, Leonardo & Amstad, Marlene & He, Chao & XIA, Fan Dora, 2021. "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," CEPR Discussion Papers 15682, C.E.P.R. Discussion Papers.
• Marcellino, Massimiliano & Stevanovic, Dalibor & Goulet Coulombe, Philippe, 2021. "Can Machine Learning Catch the COVID-19 Recession?," CEPR Discussion Papers 15867, C.E.P.R. Discussion Papers.
• Qingfeng Liu & Yang Feng, 2021. "Machine Collaboration," Papers 2105.02569, arXiv.org, revised Feb 2024.
• Korinek, Anton & Stiglitz, Joseph, 2021. "Artificial Intelligence, Globalization, and Strategies for Economic Development," CEPR Discussion Papers 15772, C.E.P.R. Discussion Papers.
• Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
• Qiutong Guo & Shun Lei & Qing Ye & Zhiyang Fang, 2021. "MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price," Papers 2105.00707, arXiv.org.
• Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
• Nekoei, Arash & Sinn, Fabian, 2021. "Human Biographical Record (HBR)," CEPR Discussion Papers 15825, C.E.P.R. Discussion Papers.
• Alessio Brini & Daniele Tantari, 2021. "Deep Reinforcement Trading with Predictable Returns," Papers 2104.14683, arXiv.org, revised May 2023.
• Heinrich, Torsten, 2021. "Epidemics in modern economies," MPRA Paper 107578, University Library of Munich, Germany.
• Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2021. "How effective is carbon pricing? A machine learning approach to policy evaluation," ZEW Discussion Papers 21-039, ZEW - Leibniz Centre for European Economic Research.
• Fershtman, Chaim & Asker, John & Pakes, Ariel, 2021. "Artificial intelligence and Pricing: The Impact of Algorithm Design," CEPR Discussion Papers 15880, C.E.P.R. Discussion Papers.