Report NEP-BIG-2018-11-05

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:

• Terry Gregory & A.M. Salomons & Ulrich Zierahn, 2018. "Racing With or Against the Machine?: Evidence from Europe," Working Papers 18-07, Utrecht School of Economics.
• John Voorheis, 2017. "Longitudinal Environmental Inequality and Environmental Gentrification: Who Gains From Cleaner Air?," CARRA Working Papers 2017-04, Center for Economic Studies, U.S. Census Bureau.
• John Voorheis, 2017. "Air Quality, Human Capital Formation and the Long-term Effects of Environmental Inequality at Birth," CARRA Working Papers 2017-05, Center for Economic Studies, U.S. Census Bureau.
• Goller, Daniel & Knaus, Michael C. & Lechner, Michael & Okasa, Gabriel, 2018. "Predicting Match Outcomes in Football by an Ordered Forest Estimator," Economics Working Paper Series 1811, University of St. Gallen, School of Economics and Political Science.
• Jie Ding & Vahid Tarokh & Yuhong Yang, 2018. "Model Selection Techniques -- An Overview," Papers 1810.09583, arXiv.org.
• Fuli Feng & Huimin Chen & Xiangnan He & Ji Ding & Maosong Sun & Tat-Seng Chua, 2018. "Enhancing Stock Movement Prediction with Adversarial Training," Papers 1810.09936, arXiv.org, revised Jun 2019.
• Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018. "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series 7259, CESifo.
• Marc Sabate Vidales & David Siska & Lukasz Szpruch, 2018. "Unbiased deep solvers for linear parametric PDEs," Papers 1810.05094, arXiv.org, revised Jan 2022.
• Chihli Hung, 2018. "Using Preference Vector Modeling to Polarity Shift for Improvement of Opinion Mining," Proceedings of International Academic Conferences 6508391, International Institute of Social and Economic Sciences.
• Avraam Tsantekidis & Nikolaos Passalis & Anastasios Tefas & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Using Deep Learning for price prediction by exploiting stationary limit order book features," Papers 1810.09965, arXiv.org.
• Shenhao Wang & Qingyi Wang & Nate Bailey & Jinhua Zhao, 2018. "Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective," Papers 1810.10465, arXiv.org, revised Sep 2019.
• Ehsan Hoseinzade & Saman Haratizadeh, 2018. "CNNPred: CNN-based stock market prediction using several data sources," Papers 1810.08923, arXiv.org.
• Youngjoon Lee & Soohyon Kim & Ki Young Park, 2018. "Deciphering Monetary Policy Committee Minutes with Text Mining Approach: A Case of South Korea," Working papers 2018rwp-132, Yonsei University, Yonsei Economics Research Institute.
• Kfir Eliaz & Ran Spiegler, 2018. "The Model Selection Curse," Papers 1810.02888, arXiv.org.