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Report NEP-BIG-2019-04-22
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:
- Fügener, A. & Grahl, J. & Gupta, A. & Ketter, W., 2019, "Cognitive challenges in human-AI collaboration: Investigating the path towards productive delegation," ERIM Report Series Research in Management, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam, number ERS-2019-003-LIS, Apr.
- Laurent Ferrara & Anna Simoni, 2019, "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working papers, Banque de France, number 717.
- Robin Niesert & Jochem Oorschot & Chris Veldhuisen & Kester Brons & Rutger-Jan Lange, , "Can Google Search Data Help Predict Macroeconomic Series?," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 19-021/III.
- Risse, Mathias, 2019, "Human Rights, Artificial Intelligence and Heideggerian Technoskepticism: The Long (Worrisome?) View," Working Paper Series, Harvard University, John F. Kennedy School of Government, number rwp19-010, Feb.
- Hieu Quang Nguyen & Abdul Hasib Rahimyar & Xiaodi Wang, 2019, "Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models," Papers, arXiv.org, number 1904.08459, Apr.
- Jan Abrell & Mirjam Kosch & Sebastian Rausch, 2019, "How Effective Was the UK Carbon Tax? — A Machine Learning Approach to Policy Evaluation," CER-ETH Economics working paper series, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich, number 19/317, Apr.
- Justin Sirignano & Rama Cont, 2018, "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers, HAL, number hal-01754054, Mar.
- Kania, Elsa B., 2018, "New Frontiers of Chinese Defense Innovation: Artificial Intelligence and Quantum Technologies," Institute on Global Conflict and Cooperation, Working Paper Series, Institute on Global Conflict and Cooperation, University of California, number qt66n8s5br, May.
- Caferra, Rocco & Morone, Andrea, 2019, "Tax Morale and Perceived Intergenerational Mobility: a Machine Learning Predictive Approach," MPRA Paper, University Library of Munich, Germany, number 93171, Apr.
- Crowley, Frank & Doran, Justin, 2019, "Automation and Irish Towns: Who's Most at Risk?," SRERC Working Paper Series, University College Cork (UCC), Spatial and Regional Economic Research Centre (SRERC), number SRERCWP2019-1.
- Brighton, Henry, 2019, "Beyond quantified ignorance: Rebuilding rationality without the bias bias," Economics Discussion Papers, Kiel Institute for the World Economy, number 2019-25.
- Pauline Affeldt, 2019, "EU Merger Policy Predictability Using Random Forests," Discussion Papers of DIW Berlin, DIW Berlin, German Institute for Economic Research, number 1800.
- Bleemer, Zachary, 2018, "The University of California ClioMetric History Project and Formatted Optical Character Recognition," University of California at Berkeley, Center for Studies in Higher Education, Center for Studies in Higher Education, UC Berkeley, number qt1xp6g8nj, Feb.
- Bing Yu & Xiaojing Xing & Agus Sudjianto, 2019, "Deep-learning based numerical BSDE method for barrier options," Papers, arXiv.org, number 1904.05921, Apr.