Report NEP-BIG-2019-05-06

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:

• Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
• Yingyao Hu & Jiaxiong Yao, 2019. "Illuminating Economic Growth," IMF Working Papers 19/77, International Monetary Fund.
• Acemoglu, Daron & Restrepo, Pascual, 2019. "The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand," IZA Discussion Papers 12292, Institute of Labor Economics (IZA).
• Peter C. B. Phillips & Zhentao Shi, 2019. "Boosting: Why You Can Use the HP Filter," Papers 1905.00175, arXiv.org, revised Nov 2020.
• Allison Koenecke & Amita Gajewar, 2019. "Curriculum Learning in Deep Neural Networks for Financial Forecasting," Papers 1904.12887, arXiv.org, revised Jul 2019.
• Yu Zheng & Yongxin Yang & Bowei Chen, 2019. "Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction," Papers 1904.12834, arXiv.org, revised May 2021.
• Sen, Sugata, 2019. "Decomposition of intra-household disparity sensitive fuzzy multi-dimensional poverty index: A study of vulnerability through Machine Learning," MPRA Paper 93550, University Library of Munich, Germany.
• John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.
• Sandra Planes-Satorra & Caroline Paunov, 2019. "The digital innovation policy landscape in 2019," OECD Science, Technology and Industry Policy Papers 71, OECD Publishing.
• Braun, Robert, 2019. "Artificial Intelligence: Socio-Political Challenges of Delegating Human Decision-Making to Machines," IHS Working Paper Series 6, Institute for Advanced Studies.
• Justine S. Hastings & Mark Howison & Sarah E. Inman, 2019. "Predicting High-Risk Opioid Prescriptions Before they are Given," NBER Working Papers 25791, National Bureau of Economic Research, Inc.
• Velibor V. Miv{s}i'c & Georgia Perakis, 2019. "Data Analytics in Operations Management: A Review," Papers 1905.00556, arXiv.org.
• Makoto Chiba & Mikari Kashima & Kenta Sekiguchi, 2019. "Legal Responsibility in Investment Decisions Using Algorithms and AI," Bank of Japan Research Laboratory Series 19-E-1, Bank of Japan.
• Pumplun, Luisa & Tauchert, Christoph & Heidt, Margareta, 2019. "A New Organizational Chassis for Artificial Intelligence - Exploring Organizational Readiness Factors," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 112582, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Otello Ardovino & Jacopo Arpetti & Marco Delmastro, 2019. "Regulating AI: do we need new tools?," Papers 1904.12134, arXiv.org.
• Jinks, Lu & Kniesner, Thomas J. & Leeth, John D. & Lo Sasso, Anthony T., 2019. "Opting out of Workers' Compensation: Non-Subscription in Texas and Its Effects," IZA Discussion Papers 12290, Institute of Labor Economics (IZA).
• Bazhenov, Timofey & Fantazzini, Dean, 2019. "Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility," MPRA Paper 93544, University Library of Munich, Germany.
• Du, Ruihuan & Zhong, Yu & Nair, Harikesh S. & Cui, Bo & Shou, Ruyang, 2019. "Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network," Research Papers 3761, Stanford University, Graduate School of Business.