Report NEP-BIG-2021-06-14

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:

• Hannes Mueller & André Groeger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2021. "Monitoring War Destruction from Space Using Machine Learning," Working Papers 1257, Barcelona School of Economics.
• Victor Klockmann & Alicia von Schenk & Marie Claire Villeval, 2021. "Artificial Intelligence, Ethics, and Diffused Pivotality," Working Papers 2111, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
• Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
• Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Bank of Israel Working Papers 2021.06, Bank of Israel.
• Saide Aránzazu Salazar & Jaime Oliver Huidobro & Alvaro Ortiz & Tomasa Rodrigo & Ignacio Tamarit, 2021. "México | Patrones de consumo de efectivo vs. tarjeta: una aproximación Big Data [Mexico | Cash vs. Card Consumption Patterns: A Machine Learning Approach]," Working Papers 21/05, BBVA Bank, Economic Research Department.
• Victor Klockmann & Alicia von Schenk & Marie Claire Villeval, 2021. "Artificial Intelligence, Ethics, and Intergenerational Responsibility," Working Papers 2110, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
• Adams-Prassl, Abigail & Boneva, Teodora & Rauh, Christopher & Golin, Marta, 2020. "Work Tasks That Can Be Done From Home: Evidence on Variation Within & Across Occupations and Industries," CEPR Discussion Papers 14901, C.E.P.R. Discussion Papers.
• Jing Xiao & Ron Boschma, 2021. "The emergence of Artificial Intelligence in European regions: the role of a local ICT base," Papers in Evolutionary Economic Geography (PEEG) 2117, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised May 2021.
• Shen, Fei & Xia, Chuanli & Yu, Wenting & Min, Chen & Wang, Tianjiao & Wu, Yi & Ye, Qianying, 2021. "The Growth of Negative Sentiment in Post-Umbrella Movement Hong Kong: Analyzing Public Opinion Online from 2017 to 2019," SocArXiv tnxw4, Center for Open Science.
• Kazutoshi Kan, 2021. "Security Risks of Machine Learning Systems and Taxonomy Based on the Failure Mode Approach," IMES Discussion Paper Series 21-E-03, Institute for Monetary and Economic Studies, Bank of Japan.
• Shen, Fei & Xia, Chuanli & Yu, Wenting & Min, Chen & Wang, Tianjiao & Wu, Yi & Ye, Qianying, 2021. "The Ebb and Flow of Public Sentiments in Hong Kong: Analyzing Public Opinion Online from 2000 to 2017," SocArXiv 52zbm, Center for Open Science.
• Rho Caterina & Fernández Raúl & Palma Brenda, 2021. "A Sentiment-based Risk Indicator for the Mexican Financial Sector," Working Papers 2021-04, Banco de México.
• Simone Vannuccini & Ekaterina Prytkova, 2021. "Artificial Intelligence’s New Clothes? From General Purpose Technology to Large Technical System," SPRU Working Paper Series 2021-02, SPRU - Science Policy Research Unit, University of Sussex Business School.
• Tiago Cravo Oliveira Hashiguchi & Luke Slawomirski & Jillian Oderkirk, 2021. "Laying the foundations for artificial intelligence in health," OECD Health Working Papers 128, OECD Publishing.
• Mateusz Buczyński & Marcin Chlebus, 2021. "GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks," Working Papers 2021-08, Faculty of Economic Sciences, University of Warsaw.
• Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.
• Koffi, Marlene, 2021. "Innovative ideas and gender inequality," CLEF Working Paper Series 35, Canadian Labour Economics Forum (CLEF), University of Waterloo.
• Klaus Ackermann & Sefa Awaworyi Churchill & Russell Smyth, 2021. "Mobile phone coverage and violent conflict," SoDa Laboratories Working Paper Series 2021-06, Monash University, SoDa Laboratories.
• Bart Cockx, 2021. "Comment améliorer l’efficacité des formations pour les demandeurs d’emploi grâce aux outils du Big Data ?," Regards économiques 160, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).