Report NEP-BIG-2019-11-25

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:

• Jamie Berryhill & Kévin Kok Heang & Rob Clogher & Keegan McBride, 2019. "Hello, World: Artificial intelligence and its use in the public sector," OECD Working Papers on Public Governance 36, OECD Publishing.
• Andrew J Tiffin, 2019. "Machine Learning and Causality: The Impact of Financial Crises on Growth," IMF Working Papers 19/228, International Monetary Fund.
• Edwards, Lilian & Veale, Michael, 2017. "Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for," LawArXiv 97upg, Center for Open Science.
• Merrill, Nathaniel & Atkinson, Sarina F. & Mulvaney, Kate K. & Mazzotta, Marisa J. & Bousquin, Justin, 2019. "Using Data Derived from Cellular Phone Locations to Estimate Visitation to Natural Areas: An Application to Water Recreation in New England, USA," SocArXiv 3nx2v, Center for Open Science.
• Veale, Michael & Edwards, Lilian, 2017. "Clarity, Surprises, and Further Questions in the Article 29 Working Party Draft Guidance on Automated Decision-Making and Profiling," LawArXiv y25ag, Center for Open Science.
• Tyler Pike & Horacio Sapriza & Tom Zimmermann, 2019. "Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning," Finance and Economics Discussion Series 2019-070, Board of Governors of the Federal Reserve System (U.S.).
• Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
• Aditya Aladangady & Shifrah Aron-Dine & Wendy E. Dunn & Laura Feiveson & Paul Lengermann & Claudia R. Sahm, 2019. "From Transactions Data to Economic Statistics: Constructing Real-time, High-frequency, Geographic Measures of Consumer Spending," Finance and Economics Discussion Series 2019-057, Board of Governors of the Federal Reserve System (U.S.).
• Richard H. Clarida, 2019. "Introductory Remarks : a speech at \"Nontraditional Data, Machine Learning, and Natural Language Processing in Macroeconomics,\" a research conference sponsored by the Federal Reserve Board,," Speech 1088, Board of Governors of the Federal Reserve System (U.S.).
• B. Shravan Kumar & Vadlamani Ravi & Rishabh Miglani, 2019. "Predicting Indian stock market using the psycho-linguistic features of financial news," Papers 1911.06193, arXiv.org.
• Daniel J. Lewis & Davide Melcangi & Laura Pilossoph, 2019. "Latent Heterogeneity in the Marginal Propensity to Consume," Staff Reports 902, Federal Reserve Bank of New York.
• Mehran Azimi & Anup Agrawal, 2019. "Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning," 2019 Papers paz108, Job Market Papers.
• Christophe HURLIN & Christophe PERIGNON, 2019. "Machine Learning et nouvelles sources de données pour le scoring de crédit," LEO Working Papers / DR LEO 2712, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
• Christoph March, 2019. "The Behavioral Economics of Artificial Intelligence: Lessons from Experiments with Computer Players," CESifo Working Paper Series 7926, CESifo.
• Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher J. Kurz, 2019. "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," Finance and Economics Discussion Series 2019-065, Board of Governors of the Federal Reserve System (U.S.).
• Stephen S. Poloz, 2019. "Technological Progress and Monetary Policy: Managing the Fourth Industrial Revolution," Discussion Papers 2019-11, Bank of Canada.
• Alevtina Repina, 2019. "Artificial Intelligence In Legal Services: State-Of-Art And Users’ Expectations In Russia," HSE Working papers WP BRP 104/STI/2019, National Research University Higher School of Economics.
• Nicholas Beale & Heather Battey & Anthony C. Davison & Robert S. MacKay, 2019. "An Unethical Optimization Principle," Papers 1911.05116, arXiv.org.
• Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
• Sally Owen & Ilan Noy & Jacob Pástor-Paz & David Fleming, 2019. "EQC and extreme weather events (part 2): Measuring the impact of insurance on New Zealand landslip, storm and flood recovery using nightlights," Working Papers 19_19, Motu Economic and Public Policy Research.
• Ojo, Marianne, 2019. "The future of UK Carbon pricing: Artificial Intelligence and the Emissions Trading System," MPRA Paper 94887, University Library of Munich, Germany.
• Harold D. Chiang, 2019. "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers pch1836, Job Market Papers.
• Yu Zheng & Bowei Chen & Timothy M. Hospedales & Yongxin Yang, 2019. "Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach," Papers 1911.05052, arXiv.org, revised Nov 2019.
• Steven Engels & Monika Sherwood, 2019. "What if We All Worked Gigs in the Cloud? The Economic Relevance of Digital Labour Platforms," European Economy - Discussion Papers 099, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
• Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Emmanouel & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2019. "Using textual analysis to identify merger participants: Evidence from the U.S. banking industry," MPRA Paper 96893, University Library of Munich, Germany.
• Stoehr, Niklas & Braesemann, Fabian & Zhou, Shi, 2019. "Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends," SocArXiv bu5zs, Center for Open Science.