Report NEP-CMP-2019-01-21

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:

• Bargain, Olivier B. & Jara Tamayo, Holguer Xavier & Magejo, Prudence & Benhura, Miracle, 2018. "Learning from the," IZA Discussion Papers 12017, Institute of Labor Economics (IZA).
• Olga Diukanova & Giovanni Mandras & Andrea Conte & Simone Salotti, 2018. "R&I and Low-carbon investment in Apulia, Italy: The RHOMOLO assessment," JRC Research Reports JRC115019, Joint Research Centre.
• Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
• Ash, Elliott & Chen, Daniel L. & Delgado, Raul & Fierro, Eduardo & Lin, Shasha, 2018. "Learning Policy Levers: Toward Automated Policy Analysis Using Judicial Corpora," TSE Working Papers 18-977, Toulouse School of Economics (TSE).
• Christian Moser & Pedro Olea de Souza e Silva, 2019. "Optimal Paternalistic Savings Policies," Opportunity and Inclusive Growth Institute Working Papers 17, Federal Reserve Bank of Minneapolis.
• Brian Ning & Franco Ho Ting Lin & Sebastian Jaimungal, 2018. "Double Deep Q-Learning for Optimal Execution," Papers 1812.06600, arXiv.org, revised Jun 2020.
• Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
• Yaodong Yang & Alisa Kolesnikova & Stefan Lessmann & Tiejun Ma & Ming-Chien Sung & Johnnie E. V. Johnson, 2018. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," Papers 1812.06175, arXiv.org, revised Nov 2019.
• Hübler, Michael & Axel Herdecke, 2019. "The US trade dispute: blunt offense or rational strategy?," Hannover Economic Papers (HEP) dp-648, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
• Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Dec 2021.
• Chen, Daniel L., 2018. "Machine Learning and the Rule of Law," TSE Working Papers 18-975, Toulouse School of Economics (TSE).
• Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," IZA Discussion Papers 12039, Institute of Labor Economics (IZA).
• Steffen Q. Mueller & Patrick Ring & Maria Schmidt, 2019. "Forecasting economic decisions under risk: The predictive importance of choice-process data," Working Papers 066, Chair for Economic Policy, University of Hamburg.
• Chen, Daniel L., 2018. "Judicial Analytics and the Great Transformation of American Law," TSE Working Papers 18-974, Toulouse School of Economics (TSE).