Report NEP-BIG-2019-11-04

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:

• Michael Coelli & Jeff Borland, 2019. "Behind the headline number: Why not to rely on Frey and Osborne’s predictions of potential job loss from automation," Melbourne Institute Working Paper Series wp2019n10, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
• Kim Kaivanto & Peng Zhang, 2019. "Popular Music, Sentiment, and Noise Trading," Working Papers 279326509, Lancaster University Management School, Economics Department.
• PRAET, Stiene & VAN AELST, Peter & MARTENS, David, 2018. "I like, therefore I am. Predictive modeling to gain insights in political preference in a multi-party system," Working Papers 2018014, University of Antwerp, Faculty of Business and Economics.
• Yvette Burton, 2019. "Keeping Real World Bias Out of Artificial Intelligence ?Examination of Coder Bias in Data Science Recruitment Solutions?," Proceedings of International Academic Conferences 9110624, International Institute of Social and Economic Sciences.
• Julia M. Puaschunder, 2019. "Towards Legal Empirical Macrodynamics: A Research Agenda," Proceedings of the 14th International RAIS Conference, August 19-20, 2019 010JP, Research Association for Interdisciplinary Studies.
• Tae-Hwy Lee & Jianghao Chu & Aman Ullah & Ran Wang, 2019. "Boosting," Working Papers 201917, University of California at Riverside, Department of Economics.
• Strittmatter, Anthony, 2019. "What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203499, Verein für Socialpolitik / German Economic Association.
• Tae-Hwy Lee & Jianghao Chu & Aman Ullah, 2018. "Component-wise AdaBoost Algorithms for High-dimensional Binary Classi fication and Class Probability Prediction," Working Papers 201907, University of California at Riverside, Department of Economics.
• Olivier Gu'eant & Iuliia Manziuk, 2019. "Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality," Papers 1910.13205, arXiv.org.
• Tae-Hwy Lee & Jianghao Chu & Aman Ullah, 2018. "Variable Selection in Sparse Semiparametric Single Index Models," Working Papers 201908, University of California at Riverside, Department of Economics.
• Hinterlang, Natascha, 2019. "Predicting Monetary Policy Using Artificial Neural Networks," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203503, Verein für Socialpolitik / German Economic Association.
• Giuseppe Carlo Calafiore & Marisa Hillary Morales & Vittorio Tiozzo & Serge Marquie, 2019. "A Classifiers Voting Model for Exit Prediction of Privately Held Companies," Papers 1910.13969, arXiv.org.
• Vladimir Puzyrev, 2019. "Deep convolutional autoencoder for cryptocurrency market analysis," Papers 1910.12281, arXiv.org.
• Bachev, Hrabrin, 2019. "Дигитализация На Селското Стопанство И Райони В България [Digitalisation of Bulgarian agriculture and rural areas]," MPRA Paper 96736, University Library of Munich, Germany.
• Saskia ter Ellen & Vegard H. Larsen & Leif Anders Thorsrud, 2019. "Narrative monetary policy surprises and the media," Working Papers No 06/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
• Tae-Hwy Lee & Aman Ullah & Ran Wang, 2019. "Bootstrap Aggregating and Random Forest," Working Papers 201918, University of California at Riverside, Department of Economics.
• IKEDA Yoko & IIZUKA Michiko, 2019. "Global Rulemaking Strategy for Implementing Emerging Innovation: Case of Medical/Healthcare Robot, HAL by Cyberdyne (Japanese)," Policy Discussion Papers (Japanese) 19016, Research Institute of Economy, Trade and Industry (RIETI).