Report NEP-CMP-2019-04-08

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:

• Rosdyana Mangir Irawan Kusuma & Trang-Thi Ho & Wei-Chun Kao & Yu-Yen Ou & Kai-Lung Hua, 2019. "Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market," Papers 1903.12258, arXiv.org.
• Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.
• Marco, Jorge & Goetz, Renan, 2017. "Tragedy of the Commons and Evolutionary Games in Social Networks: The Economics of Social Punishment," ETA: Economic Theory and Applications 259486, Fondazione Eni Enrico Mattei (FEEM).
• Karun Adusumilli & Friedrich Geiecke & Claudio Schilter, 2019. "Dynamically Optimal Treatment Allocation," Papers 1904.01047, arXiv.org, revised Nov 2024.
• Alexander Kindel & Vineet Bansal & Kristin Catena & Thomas Hartshorne & Kate Jaeger, 2018. "Improving metadata infrastructure for complex surveys: 
Insights from the Fragile Families Challenge," Working Papers wp18-10-ff, Princeton University, School of Public and International Affairs, Center for Research on Child Wellbeing..
• Fischer, Daniel & Berro, Alain & Nordhausen, Klaus & Ruiz-Gazen, Anne, 2019. "REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit," TSE Working Papers 19-1001, Toulouse School of Economics (TSE).
• Strittmatter, Anthony, 2019. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," GLO Discussion Paper Series 336, Global Labor Organization (GLO).
• Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
• Zubarev, Andrey (Зубарев, Андрей) & Nesterova, Kristina (Нестерова, Кристина), 2019. "Modeling the increase in the retirement age in the Russian economy using the global CGE-OLG model [Моделирование Повышения Пенсионного Возраста В Российские Экономики С Помощью Глобальной Cge-Olg М," Working Papers 031952, Russian Presidential Academy of National Economy and Public Administration.
• Naude, Wim, 2019. "The race against the robots and the fallacy of the giant cheesecake: Immediate and imagined impacts of artificial intelligence," MERIT Working Papers 2019-005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
• Vladimir Markov, 2019. "Bayesian Trading Cost Analysis and Ranking of Broker Algorithms," Papers 1904.01566, arXiv.org, revised Apr 2019.