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Report NEP-BIG-2026-02-23
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:
- Nicholas Lacoste & Zehra Farooq, 2026, "Optimal Audit Targeting with Machine Learning: Evidence from Pakistan," Working Papers, Tulane University, Department of Economics, number 2603, Feb.
- Chouech, Olfa, 2025, "Predicting Corporate ESG Scores from Financial Performance and Environmental Indicators: A Machine Learning Framework," MPRA Paper, University Library of Munich, Germany, number 127272, Sep, revised 10 Dec 2025.
- Bouillot, Roland & Candelon, Bertrand & Kool, Clemens, 2025, "Forecasting European Sovereign Spreads using Machine Learning," LIDAM Discussion Papers LFIN, Université catholique de Louvain, Louvain Finance (LFIN), number 2025004, Nov.
- M. Merritt Smith & Emily Aiken & Joshua E. Blumenstock & Sveta Milusheva, 2026, "Predicting Well-Being with Mobile Phone Data: Evidence from Four Countries," Papers, arXiv.org, number 2602.02805, Feb.
- Kittel, Rebecca & Silva, Bruno Castanho, 2026, "Keep it simple, stupid!: The determinants of language complexity in politicians' parliamentary and online communication," Discussion Papers, Research Unit: Center for Civil Society Research, WZB Berlin Social Science Center, number ZZ 2026-601.