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Report NEP-BIG-2019-09-09
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:
- Denis Shibitov & Mariam Mamedli, 2019, "The finer points of model comparison in machine learning: forecasting based on russian banks’ data," Bank of Russia Working Paper Series, Bank of Russia, number wps43, Aug.
- Samuel Asante Gyamerah, 2019, "Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators," Papers, arXiv.org, number 1909.01268, Sep.
- Haoqian Li & Thomas Lau, 2019, "Reinforcement Learning: Prediction, Control and Value Function Approximation," Papers, arXiv.org, number 1908.10771, Aug.
- Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2019, "Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germany's Programmes for Long Term Unemployed," IZA Discussion Papers, IZA Network @ LISER, number 12526, Aug.
- Chen, Jian & Katchova, Ani, , "Agricultural Loan Delinquency Prediction Using Machine Learning Methods," 2019 Annual Meeting, July 21-23, Atlanta, Georgia, Agricultural and Applied Economics Association, number 290745, DOI: 10.22004/ag.econ.290745.
- Makoto YANO & Yuichi FURUKAWA, 2019, "Economic Black Holes and Labor Singularities in the Presence of Self-replicating Artificial Intelligence," Discussion papers, Research Institute of Economy, Trade and Industry (RIETI), number 19062, Aug.
- Huber, Martin, 2019, "An introduction to flexible methods for policy evaluation," FSES Working Papers, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland, number 504, Aug.
- Kerda Varaku, 2019, "Stock Price Forecasting and Hypothesis Testing Using Neural Networks," Papers, arXiv.org, number 1908.11212, Aug.
- Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019, "Predicting Returns With Text Data," NBER Working Papers, National Bureau of Economic Research, Inc, number 26186, Aug.
- Item repec:bof:bofrdp:2019_014 is not listed on IDEAS anymore
- Francisco C. Pereira, 2019, "Rethinking travel behavior modeling representations through embeddings," Papers, arXiv.org, number 1909.00154, Aug.
- Julia M. Puaschunder, 2018, "Towards a Utility Theory of Privacy and Information Sharing and the Introduction of Hyper-Hyperbolic Discounting in the Digital Big Data Age," RAIS Collective Volume – Economic Science, Research Association for Interdisciplinary Studies, number 01.
- Alessio Arleo & Christos Tsigkanos & Chao Jia & Roger A. Leite & Ilir Murturi & Manfred Klaffenboeck & Schahram Dustdar & Michael Wimmer & Silvia Miksch & Johannes Sorger, 2019, "Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge," Papers, arXiv.org, number 1908.07479, Aug, revised Jan 2020.
- Lindquist, Matthew J. & Zenou, Yves, 2019, "Crime and Networks: 10 Policy Lessons," IZA Discussion Papers, IZA Network @ LISER, number 12534, Aug.
- Stefania Albanesi & Domonkos F. Vamossy, 2019, "Predicting Consumer Default: A Deep Learning Approach," Papers, arXiv.org, number 1908.11498, Aug, revised Oct 2019.
- Zhou, Yujun & Baylis, Kathy, , "Predict Food Security with Machine Learning: Application in Eastern Africa," 2019 Annual Meeting, July 21-23, Atlanta, Georgia, Agricultural and Applied Economics Association, number 291056, DOI: 10.22004/ag.econ.291056.
- G'abor Petneh'azi, 2019, "Quantile Convolutional Neural Networks for Value at Risk Forecasting," Papers, arXiv.org, number 1908.07978, Aug, revised Sep 2020.
- Wolfgang Kerber, 2019, "Data-sharing in IoT Ecosystems from a Competition Law Perspective: The Example of Connected Cars," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 201921.