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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 (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:
- Athey, Susan & Imbens, Guido W., 2019, "Machine Learning Methods Economists Should Know About," Research Papers, Stanford University, Graduate School of Business, number 3776, Mar.
- Item repec:imf:imfwpa:19/77 is not listed on IDEAS anymore
- Acemoglu, Daron & Restrepo, Pascual, 2019, "The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand," IZA Discussion Papers, Institute of Labor Economics (IZA), number 12292, Apr.
- Peter C. B. Phillips & Zhentao Shi, 2019, "Boosting: Why You Can Use the HP Filter," Papers, arXiv.org, number 1905.00175, Apr, revised Nov 2020.
- Allison Koenecke & Amita Gajewar, 2019, "Curriculum Learning in Deep Neural Networks for Financial Forecasting," Papers, arXiv.org, number 1904.12887, Apr, revised Jul 2019.
- Yu Zheng & Yongxin Yang & Bowei Chen, 2019, "Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction," Papers, arXiv.org, number 1904.12834, Apr, 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, University Library of Munich, Germany, number 93550, Apr.
- John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019, "Supervised Machine Learning for Eliciting Individual Demand," Papers, arXiv.org, number 1904.13329, Apr, revised Feb 2021.
- Sandra Planes-Satorra & Caroline Paunov, 2019, "The digital innovation policy landscape in 2019," OECD Science, Technology and Industry Policy Papers, OECD Publishing, number 71, May, DOI: 10.1787/6171f649-en.
- Braun, Robert, 2019, "Artificial Intelligence: Socio-Political Challenges of Delegating Human Decision-Making to Machines," IHS Working Paper Series, Institute for Advanced Studies, number 6, Apr.
- Justine S. Hastings & Mark Howison & Sarah E. Inman, 2019, "Predicting High-Risk Opioid Prescriptions Before they are Given," NBER Working Papers, National Bureau of Economic Research, Inc, number 25791, Apr.
- Velibor V. Miv{s}i'c & Georgia Perakis, 2019, "Data Analytics in Operations Management: A Review," Papers, arXiv.org, number 1905.00556, May.
- Makoto Chiba & Mikari Kashima & Kenta Sekiguchi, 2019, "Legal Responsibility in Investment Decisions Using Algorithms and AI," Bank of Japan Research Laboratory Series, Bank of Japan, number 19-E-1, Apr.
- 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), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 112582, Jun.
- Otello Ardovino & Jacopo Arpetti & Marco Delmastro, 2019, "Regulating AI: do we need new tools?," Papers, arXiv.org, number 1904.12134, Apr.
- 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, Institute of Labor Economics (IZA), number 12290, Apr.
- Bazhenov, Timofey & Fantazzini, Dean, 2019, "Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility," MPRA Paper, University Library of Munich, Germany, number 93544, Apr.
- 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, Stanford University, Graduate School of Business, number 3761, Jan.