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Report NEP-BIG-2021-10-04
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:
- Nguyen, Phong Thanh, 2020, "Application Machine Learning in Construction Management," MPRA Paper, University Library of Munich, Germany, number 109899, Dec, revised 01 Aug 2021.
- Bali, Turan G. & Beckmeyer, Heiner & Moerke, Mathis & Weigert, Florian, 2021, "Option return predictability with machine learning and big data," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 21-08.
- Xingzuo Zhou & Yiang Li, 2021, "Forecasting the COVID-19 vaccine uptake rate: An infodemiological study in the US," Papers, arXiv.org, number 2109.13971, Sep, revised Dec 2021.
- Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2021, "Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility," Papers, arXiv.org, number 2109.12621, Sep.
- Roland Tricot, 2021, "Venture capital investments in artificial intelligence: Analysing trends in VC in AI companies from 2012 through 2020," OECD Digital Economy Papers, OECD Publishing, number 319, Sep, DOI: 10.1787/f97beae7-en.
- Fatime Barbara Hegyi & Manran Zhu & Milan Janosov, 2021, "Measuring the Impact of Urban Innovation Districts," JRC Research Reports, Joint Research Centre, number JRC125559, Sep.
- Gharad T. Bryan & Dean Karlan & Adam Osman, 2021, "Big Loans to Small Businesses: Predicting Winners and Losers in an Entrepreneurial Lending Experiment," NBER Working Papers, National Bureau of Economic Research, Inc, number 29311, Sep.
- Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021, "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers, arXiv.org, number 2109.12042, Sep, revised Sep 2021.
- Angell, Mintaka & Gold, Samantha & Hastings, Justine S. & Howison, Mark & Jensen, Scott & Keleher, Niall & Molitor, Daniel & Roberts, Amelia, 2021, "Estimating value-added returns to labor training programs with causal machine learning," OSF Preprints, Center for Open Science, number thg23, Sep, DOI: 10.31219/osf.io/thg23.
- Pengzhou Wu & Kenji Fukumizu, 2021, "Towards Principled Causal Effect Estimation by Deep Identifiable Models," Papers, arXiv.org, number 2109.15062, Sep, revised Nov 2021.
- Genz, Sabrina & Gregory, Terry & Janser, Markus & Lehmer, Florian & Matthes, Britta, 2021, "How do workers adjust when firms adopt new technologies?," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 21-073.
- Jaeyoung Cheong & Heejoon Lee & Minjung Kang, 2021, "Stock Index Prediction using Cointegration test and Quantile Loss," Papers, arXiv.org, number 2109.15045, Sep.
- Olubusoye, Olusanya E & Akintande, Olalekan J. & Yaya, OlaOluwa S. & Ogbonna, Ahamuefula & Adenikinju, Adeola F., 2021, "Energy Pricing during the COVID-19 Pandemic: Predictive Information-Based Uncertainty Indexes with Machine Learning Algorithm," MPRA Paper, University Library of Munich, Germany, number 109838, Sep.
- Nicolas Gavoille & Anna Zasova, 2021, "What we pay in the shadow: Labor tax evasion, minimum wage hike and employment," Working Papers CEB, ULB -- Universite Libre de Bruxelles, number 21-017, Sep.
- Shuo Sun & Rundong Wang & Bo An, 2021, "Reinforcement Learning for Quantitative Trading," Papers, arXiv.org, number 2109.13851, Sep.
- Nicolas Gavoille & Anna Zasova, 2021, "What we pay in the shadows: Labor tax evasion, minimum wage hike and employment," SSE Riga/BICEPS Research Papers, Baltic International Centre for Economic Policy Studies (BICEPS);Stockholm School of Economics in Riga (SSE Riga), number 6, Aug.
- Kea BARET, 2021, "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg, number 2021-38.
- Wurm, Daniel & Zielinski, Oliver & Lübben, Neeske & Jansen, Maike & Ramesohl, Stephan, 2021, "Wege in eine ökologische Machine Economy: Wir brauchen eine 'Grüne Governance der Machine Economy', um das Zusammenspiel von Internet of Things, Künstlicher Intelligenz und Distributed Ledger Technology ökologisch zu gestalten," Wuppertal Reports, Wuppertal Institute for Climate, Environment and Energy, number 22, DOI: 10.48506/opus-7828.
- Claude Crampes & Yassine Lefouili, 2021, "Green energy pricing for digital europe," Post-Print, HAL, number hal-03352748, Sep.
- Pedro Garcia-del-Bario & J. James Reade, 2021, "Does Certainty on the Winner Diminish the Interest in Sport Competitions? The Case of Formula One," Economics Discussion Papers, Department of Economics, University of Reading, number em-dp2021-18, Sep.
- Hélia Costa & Giuseppe Nicoletti & Mauro Pisu & Christina von Rueden, 2021, "Welcome to the (digital) jungle: Measuring online platform diffusion," OECD Economics Department Working Papers, OECD Publishing, number 1683, Oct, DOI: 10.1787/b4e771d7-en.
- G. Mazzei & F. G. Bellora & J. A. Serur, 2021, "Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets," Papers, arXiv.org, number 2109.12337, Sep.
- Prem, Mounu & Purroy, Miguel E. & Vargas, Juan F., 2021, "Landmines: The Local Effects of Demining," SocArXiv, Center for Open Science, number 3jzk6, Sep, DOI: 10.31219/osf.io/3jzk6.
- Chongwoo Choe & Jiajia Cong & Chengsi Wang, 2021, "Softening Competition through Unilateral Sharing of Customer Data," Monash Economics Working Papers, Monash University, Department of Economics, number 2021-10, Sep.