Report NEP-BIG-2019-08-26

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• Decarolis, Francesco & Rovigatti, Gabriele, 2019. "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising," CEPR Discussion Papers 13897, C.E.P.R. Discussion Papers.
• Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
• Gopal K. Basak & Pranab Kumar Das & Sugata Marjit & Debashis Mukherjee & Lei Yang, 2019. "British Stock Market, BREXIT and Media Sentiments - A Big Data Analysis," CESifo Working Paper Series 7760, CESifo.
• Loermann, Julius & Maas, Benedikt, 2019. "Nowcasting US GDP with artificial neural networks," MPRA Paper 95459, University Library of Munich, Germany.
• Anna Stelzer, 2019. "Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions," Papers 1907.12996, arXiv.org.
• Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
• Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
• Jordan Vazquez & Cécile Godé & Jean-Fabrice Lebraty, 2019. "Environnement big data et prise de décision intuitive : le cas de la Police Nationale des Bouches du Rhône," Post-Print halshs-02188451, HAL.
• Halminen, Olli & Tenhunen, Henni & Heliste, Antti & Seppälä, Timo, 2019. "Artificial Intelligence Applications & Venture Funding in Healthcare," ETLA Working Papers 68, The Research Institute of the Finnish Economy.
• Ningyuan Chen & Guillermo Gallego & Zhuodong Tang, 2019. "The Use of Binary Choice Forests to Model and Estimate Discrete Choices," Papers 1908.01109, arXiv.org, revised Apr 2024.
• Zhentao Shi & Jingyi Huang, 2019. "Forward-Selected Panel Data Approach for Program Evaluation," Papers 1908.05894, arXiv.org, revised Apr 2021.
• Haoran Wang, 2019. "Large scale continuous-time mean-variance portfolio allocation via reinforcement learning," Papers 1907.11718, arXiv.org, revised Aug 2019.
• Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2019. "Estimation of Conditional Average Treatment Effects with High-Dimensional Data," Papers 1908.02399, arXiv.org, revised Jul 2021.
• Jordan Vazquez & Cécile Godé & Jean-Fabrice Lebraty, 2018. "Environnement big data et décision : l'étape de contre la montre du tour de France 2017," Post-Print halshs-02188793, HAL.
• Badruddoza, Syed & Amin, Modhurima D., 2019. "Determining the Importance of an Attribute in a Demand System: Structural versus Machine Learning Approach," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 291210, Agricultural and Applied Economics Association.
• Shen, Ze & Wan, Qing & Leatham, David J., 2019. "Bitcoin Return Volatility Forecasting: A Comparative Study of GARCH Model and Machine Learning Model," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 290696, Agricultural and Applied Economics Association.
• Sendhil Mullainathan & Ziad Obermeyer, 2019. "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care," NBER Working Papers 26168, National Bureau of Economic Research, Inc.
• Terry McKinley, 2019. "Worried about the fourth industrial revolution's impact on jobs? Scale up skills development and training!," One Pager 425, International Policy Centre for Inclusive Growth.
• Idha Sudianto, 2019. "Review of the Plan for Integrating Big Data Analytics Program for the Electronic Marketing System and Customer Relationship Management: A Case Study XYZ Institution," Papers 1908.02430, arXiv.org.
• Maciej Berk{e}sewicz & Greta Bia{l}kowska & Krzysztof Marcinkowski & Magdalena Ma'slak & Piotr Opiela & Robert Pater & Katarzyna Zadroga, 2019. "Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration," Papers 1908.06731, arXiv.org.
• Shan Huang, 2019. "Taxable Stock Trading with Deep Reinforcement Learning," Papers 1907.12093, arXiv.org, revised Jul 2019.
• David Drukker, 2019. "Inference after lasso model selection," 2019 Stata Conference 3, Stata Users Group.
• Di Liu, 2019. "Using lasso and related estimators for prediction," 2019 Stata Conference 2, Stata Users Group.
• D'Agostino, Mollie C. & Pellaton, Paige & Brown, Austin, 2019. "Mobility Data Sharing: Challenges and Policy Recommendations," Institute of Transportation Studies, Working Paper Series qt4gw8g9ms, Institute of Transportation Studies, UC Davis.