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 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:

• 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 wps43, Bank of Russia.
• Samuel Asante Gyamerah, 2019. "Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators," Papers 1909.01268, arXiv.org.
• Haoqian Li & Thomas Lau, 2019. "Reinforcement Learning: Prediction, Control and Value Function Approximation," Papers 1908.10771, arXiv.org.
• 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 12526, Institute of Labor Economics (IZA).
• Chen, Jian & Katchova, Ani, 2019. "Agricultural Loan Delinquency Prediction Using Machine Learning Methods," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 290745, Agricultural and Applied Economics Association.
• YANO Makoto & FURUKAWA Yuichi, 2019. "Economic Black Holes and Labor Singularities in the Presence of Self-replicating Artificial Intelligence," Discussion papers 19062, Research Institute of Economy, Trade and Industry (RIETI).
• Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
• Kerda Varaku, 2019. "Stock Price Forecasting and Hypothesis Testing Using Neural Networks," Papers 1908.11212, arXiv.org.
• Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019. "Predicting Returns With Text Data," NBER Working Papers 26186, National Bureau of Economic Research, Inc.
• Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
• Francisco C. Pereira, 2019. "Rethinking travel behavior modeling representations through embeddings," Papers 1909.00154, arXiv.org.
• 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 01, Research Association for Interdisciplinary Studies.
• 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 1908.07479, arXiv.org, revised Jan 2020.
• Lindquist, Matthew J. & Zenou, Yves, 2019. "Crime and Networks: 10 Policy Lessons," IZA Discussion Papers 12534, Institute of Labor Economics (IZA).
• Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Papers 1908.11498, arXiv.org, revised Oct 2019.
• Zhou, Yujun & Baylis, Kathy, 2019. "Predict Food Security with Machine Learning: Application in Eastern Africa," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 291056, Agricultural and Applied Economics Association.
• G'abor Petneh'azi, 2019. "Quantile Convolutional Neural Networks for Value at Risk Forecasting," Papers 1908.07978, arXiv.org, 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 201921, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).