Printed from https://ideas.repec.org/n/nep-big/2020-04-27.html
Report NEP-BIG-2020-04-27
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:
- Cheng, Cindy & Barceló, Joan & Hartnett, Allison & Kubinec, Robert & Messerschmidt, Luca, 2020, "CoronaNet: A Dyadic Dataset of Government Responses to the COVID-19 Pandemic," SocArXiv, Center for Open Science, number dkvxy, Apr, DOI: 10.31219/osf.io/dkvxy.
- Naudé, Wim, 2020, "Artificial Intelligence against COVID-19: An Early Review," IZA Discussion Papers, IZA Network @ LISER, number 13110, Apr.
- Commander, Simon & Poupakis, Stavros, 2020, "Political Networks across the Globe," IZA Discussion Papers, IZA Network @ LISER, number 13103, Mar.
- Daniel Aaronson & Scott A. Brave & R. Andrew Butters & Daniel W. Sacks & Boyoung Seo, 2020, "Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims," Working Paper Series, Federal Reserve Bank of Chicago, number WP 2020-10, Apr, DOI: 10.21033/wp-2020-10.
- Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher J. Kurz, 2020, "Tracking Labor Market Developments during the COVID-19 Pandemic: A Preliminary Assessment," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-030, Apr, DOI: 10.17016/FEDS.2020.030.
- Adair Morse & Karen M. Pence, 2020, "Technological Innovation and Discrimination in Household Finance," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-018, Feb, DOI: 10.17016/FEDS.2020.018.
- Mélanie Bourassa Forcier & Lara Khoury & Nathalie Vézina, 2020, "The Use of Artificial Intelligence in Health Care: Liability Issues," CIRANO Working Papers, CIRANO, number 2020s-19, Apr.
- de Pedraza, Pablo & Vollbracht, Ian, 2020, "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series, Global Labor Organization (GLO), number 515.
- Jennifer Castle & Jurgen Doornik & David Hendry, 2020, "Modelling Non-stationary 'Big Data'," Economics Series Working Papers, University of Oxford, Department of Economics, number 905, Apr.
- Brunori, Paolo & Neidhöfer, Guido, 2020, "The evolution of inequality of opportunity in Germany: A machine learning approach," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 20-013.
- Nataliia Ostapenko, 2020, "Identification Of Monetary Policy Shocks From Fomc Transcripts," University of Tartu - Faculty of Economics and Business Administration Working Paper Series, Faculty of Economics and Business Administration, University of Tartu (Estonia), number 123.
- Ari, Anil & Ratnovski, Lev & Chen, Sophia, 2020, "The dynamics of non-performing loans during banking crises: a new database," Working Paper Series, European Central Bank, number 2395, Apr.
- Bachev, Hrabrin, 2020, "State, development and efficiency of digitalization in Bulgarian agriculture," MPRA Paper, University Library of Munich, Germany, number 99649, Mar.
- Amanda Bayer & Gregory Bruich & Raj Chetty & Andrew Housiaux, 2020, "Expanding and Diversifying the Pool of Undergraduates who Study Economics: Insights from a New Introductory Course at Harvard," NBER Working Papers, National Bureau of Economic Research, Inc, number 26961, Apr.
- Kenney, Martin & Serhan, Hiam & Trystram, Gilles, 2020, "Digitalization and Platforms in Agriculture: Organizations, Power Asymmetry, and Collective Action Solutions," ETLA Working Papers, The Research Institute of the Finnish Economy, number 78, Apr.
- Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020, "Robust Discovery of Regression Models," Economics Papers, Economics Group, Nuffield College, University of Oxford, number 2020-W04, Apr.
- Kaiser, Ulrich & Kuhn, Johan M., 2020, "The value of publicly available, textual and non-textual information for startup performance prediction," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 20-012.