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 (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:

• 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, OECD Publishing, number 36, Nov, DOI: 10.1787/726fd39d-en.
• Item repec:imf:imfwpa:19/228 is not listed on IDEAS anymore

• Edwards, Lilian & Veale, Michael, 2017, "Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for," LawRxiv, Center for Open Science, number 97upg, Nov, DOI: 10.31219/osf.io/97upg.
• 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, Center for Open Science, number 3nx2v, Nov, DOI: 10.31219/osf.io/3nx2v.
• Veale, Michael & Edwards, Lilian, 2017, "Clarity, Surprises, and Further Questions in the Article 29 Working Party Draft Guidance on Automated Decision-Making and Profiling," LawRxiv, Center for Open Science, number y25ag, Nov, DOI: 10.31219/osf.io/y25ag.
• 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, Board of Governors of the Federal Reserve System (U.S.), number 2019-070, Sep, DOI: 10.17016/FEDS.2019.070.
• Gary S. Anderson & Alena Audzeyeva, 2019, "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-074, Oct, DOI: 10.17016/FEDS.2019.074.
• 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, Board of Governors of the Federal Reserve System (U.S.), number 2019-057, Aug, DOI: 10.17016/FEDS.2019.057.
• 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, Washington, D.C., October 1, 2019," Speech, Board of Governors of the Federal Reserve System (U.S.), number 1088, Oct.
• B. Shravan Kumar & Vadlamani Ravi & Rishabh Miglani, 2019, "Predicting Indian stock market using the psycho-linguistic features of financial news," Papers, arXiv.org, number 1911.06193, Nov.
• Daniel J. Lewis & Davide Melcangi & Laura Pilossoph, 2019, "Latent Heterogeneity in the Marginal Propensity to Consume," Staff Reports, Federal Reserve Bank of New York, number 902, Nov.
• Mehran Azimi & Anup Agrawal, 2019, "Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning," 2019 Papers, Job Market Papers, number paz108, Aug.
• Item repec:leo:wpaper:2712 is not listed on IDEAS anymore

• Christoph March, 2019, "The Behavioral Economics of Artificial Intelligence: Lessons from Experiments with Computer Players," CESifo Working Paper Series, CESifo, number 7926.
• 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, Board of Governors of the Federal Reserve System (U.S.), number 2019-065, Sep, DOI: 10.17016/FEDS.2019.065.
• Stephen S. Poloz, 2019, "Technological Progress and Monetary Policy: Managing the Fourth Industrial Revolution," Discussion Papers, Bank of Canada, number 2019-11, Nov, DOI: 10.34989/sdp-2019-11.
• Item repec:hig:wpaper:104sti2019 is not listed on IDEAS anymore

• Nicholas Beale & Heather Battey & Anthony C. Davison & Robert S. MacKay, 2019, "An Unethical Optimization Principle," Papers, arXiv.org, number 1911.05116, Nov.
• Johannes Ruf & Weiguan Wang, 2019, "Neural networks for option pricing and hedging: a literature review," Papers, arXiv.org, number 1911.05620, Nov, 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," Motu Working Papers, Motu Economic and Public Policy Research, number 19_19, Nov.
• Ojo, Marianne, 2019, "The future of UK Carbon pricing: Artificial Intelligence and the Emissions Trading System," MPRA Paper, University Library of Munich, Germany, number 94887, Jul.
• Harold D. Chiang, 2019, "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers, Job Market Papers, number pch1836, Oct.
• Yu Zheng & Bowei Chen & Timothy M. Hospedales & Yongxin Yang, 2019, "Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach," Papers, arXiv.org, number 1911.05052, Nov, 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, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission, number 099, Jun.
• 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, University Library of Munich, Germany, number 96893, Nov.
• Stoehr, Niklas & Braesemann, Fabian & Zhou, Shi, 2019, "Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends," SocArXiv, Center for Open Science, number bu5zs, Oct, DOI: 10.31219/osf.io/bu5zs.

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 (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: