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 (Tom Coupe) 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, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne, number wp2019n10, Oct.
• Kim Kaivanto & Peng Zhang, 2019, "Popular Music, Sentiment, and Noise Trading," Working Papers, Lancaster University Management School, Economics Department, number 279326509.
• 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, University of Antwerp, Faculty of Business and Economics, number 2018014, Dec.
• 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, International Institute of Social and Economic Sciences, number 9110624, Jul.
• Julia M. Puaschunder, 2019, "Towards Legal Empirical Macrodynamics: A Research Agenda," Proceedings of the 14th International RAIS Conference, August 19-20, 2019, Research Association for Interdisciplinary Studies, number 010JP, Aug.
• Tae-Hwy Lee & Jianghao Chu & Aman Ullah & Ran Wang, 2019, "Boosting," Working Papers, University of California at Riverside, Department of Economics, number 201917, May.
• 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, Verein für Socialpolitik / German Economic Association, number 203499.
• Tae-Hwy Lee & Jianghao Chu & Aman Ullah, 2018, "Component-wise AdaBoost Algorithms for High-dimensional Binary Classi fication and Class Probability Prediction," Working Papers, University of California at Riverside, Department of Economics, number 201907, Jul.
• Olivier Gu'eant & Iuliia Manziuk, 2019, "Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality," Papers, arXiv.org, number 1910.13205, Oct.
• Tae-Hwy Lee & Jianghao Chu & Aman Ullah, 2018, "Variable Selection in Sparse Semiparametric Single Index Models," Working Papers, University of California at Riverside, Department of Economics, number 201908, Sep.
• 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, Verein für Socialpolitik / German Economic Association, number 203503.
• Giuseppe Carlo Calafiore & Marisa Hillary Morales & Vittorio Tiozzo & Serge Marquie, 2019, "A Classifiers Voting Model for Exit Prediction of Privately Held Companies," Papers, arXiv.org, number 1910.13969, Oct.
• Vladimir Puzyrev, 2019, "Deep convolutional autoencoder for cryptocurrency market analysis," Papers, arXiv.org, number 1910.12281, Oct.
• Bachev, Hrabrin, 2019, "Дигитализация На Селското Стопанство И Райони В България
  [Digitalisation of Bulgarian agriculture and rural areas]," MPRA Paper, University Library of Munich, Germany, number 96736, Oct.
• Saskia ter Ellen & Vegard H. Larsen & Leif Anders Thorsrud, 2019, "Narrative monetary policy surprises and the media," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 06/2019, Oct.
• Tae-Hwy Lee & Aman Ullah & Ran Wang, 2019, "Bootstrap Aggregating and Random Forest," Working Papers, University of California at Riverside, Department of Economics, number 201918, Jul.
• Yoko IKEDA & Michiko IIZUKA, 2019, "Global Rulemaking Strategy for Implementing Emerging Innovation: Case of Medical/Healthcare Robot, HAL by Cyberdyne (Japanese)," Policy Discussion Papers (Japanese), Research Institute of Economy, Trade and Industry (RIETI), number 19016, Oct.

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 (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report: