Report NEP-BIG-2019-09-16

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:

• Pierre L Siklos, 2019. "US monetary policy since the 1950s and the changing content of FOMC minutes," CAMA Working Papers 2019-69, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
• Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
• Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction Of Investor Interest: a Supervised Clustering Approach," Working Papers hal-02276055, HAL.
• Rahul Singh & Liyang Sun, 2019. "Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics," Papers 1909.05244, arXiv.org, revised Dec 2022.
• Samuel Showalter & Jeffrey Gropp, 2019. "Validating Weak-form Market Efficiency in United States Stock Markets with Trend Deterministic Price Data and Machine Learning," Papers 1909.05151, arXiv.org.
• Julia M. Puaschunder, 2019. "On Artificial Intelligence’s Razor’s Edge: On the Future of Democracy and Society in the Artificial Age," Proceedings of the 12th International RAIS Conference, April 3-4, 2019 5JP, Research Association for Interdisciplinary Studies.
• Mi Chuanmin & Xu Runjie & Lin Qingtong, 2019. "Systemic Risk Clustering of China Internet Financial Based on t-SNE Machine Learning Algorithm," Papers 1909.03808, arXiv.org.
• Elda Xhumari & Julian Fejzaj, 2019. "Usage of artificial neural networks in data classification," Proceedings of International Academic Conferences 9211565, International Institute of Social and Economic Sciences.
• Wonsup Shin & Seok-Jun Bu & Sung-Bae Cho, 2019. "Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning," Papers 1909.03278, arXiv.org.
• Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2019. "Multiway Cluster Robust Double/Debiased Machine Learning," Papers 1909.03489, arXiv.org, revised Mar 2020.
• Musabbir Chowdhury, 2019. "Strategic Realignment within Smart Ecosystems: Organizational Preparedness for Smart Cities and the Sharing Economy," Proceedings of International Academic Conferences 8710707, International Institute of Social and Economic Sciences.
• Joseph Price & Kasey Buckles & Jacob Van Leeuwen & Isaac Riley, 2019. "Combining Family History and Machine Learning to Link Historical Records," NBER Working Papers 26227, National Bureau of Economic Research, Inc.
• Julia M. Puaschunder, 2019. "Artificial Intelligence Market Disruption," Proceedings of the 13th International RAIS Conference, June 10-11, 2019 01 JP, Research Association for Interdisciplinary Studies.
• Nicolaj S{o}ndergaard Muhlbach & Mikkel Slot Nielsen, 2019. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," Papers 1909.03968, arXiv.org, revised Feb 2021.
• Huang, Xu & Hu, Yan & Dong, Zhiqiang, 2019. "The macroeconomic consequences of artificial intelligence: A theoretical framework," Economics Discussion Papers 2019-48, Kiel Institute for the World Economy (IfW Kiel).
• Arezoo Hatefi Ghahfarrokhi & Mehrnoush Shamsfard, 2019. "Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions," Papers 1909.03792, arXiv.org, revised Sep 2019.
• Masahiro Kato, 2019. "Identifying Different Definitions of Future in the Assessment of Future Economic Conditions: Application of PU Learning and Text Mining," Papers 1909.03348, arXiv.org, revised Apr 2020.
• Anne-Sophie Krah & Zoran Nikoli'c & Ralf Korn, 2019. "Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies," Papers 1909.02182, arXiv.org.
• G'abor Petneh'azi & J'ozsef G'all, 2019. "Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?," Papers 1909.05501, arXiv.org, revised Oct 2019.
• Alda Kika & Loreta Leka & Suela Maxhelaku & Ana Ktona, 2019. "Using data mining techniques on Moodle data for classification of student?s learning styles," Proceedings of International Academic Conferences 9211567, International Institute of Social and Economic Sciences.
• Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
• Susan Athey & Guido Imbens & Jonas Metzger & Evan Munro, 2019. "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations," Papers 1909.02210, arXiv.org, revised Jul 2020.
• Adam Villa, 2019. "Teaching with Tableau: Infusing Analytics into your Course," Proceedings of International Academic Conferences 9011181, International Institute of Social and Economic Sciences.
• Nicholas Illenberger & Dylan S. Small & Pamela A. Shaw, 2019. "Regression to the Mean's Impact on the Synthetic Control Method: Bias and Sensitivity Analysis," Papers 1909.04706, arXiv.org.
• Chihli Hung & Jheng-Hua Huang, 2019. "Developing an Adaptive Chinese Near-Synonym Corpus for Word of Mouth Classification," Proceedings of International Academic Conferences 8711023, International Institute of Social and Economic Sciences.
• Pierre-Alexandre Balland & Ron Boschma, 2019. "Mapping the potential of EU regions to contribute to Industry 4.0," Papers in Evolutionary Economic Geography (PEEG) 1925, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Sep 2019.
• Ji, Yongjie, 2019. "Misclassification Errors in Remote Sensing Data and Land Use Modeling," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 291218, Agricultural and Applied Economics Association.