Report NEP-BIG-2026-07-20

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.

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Other reports in NEP-BIG

The following items were announced in this report:

• Christopher W. Karvetski & Sheldon S. Huang & Simas Kuv{c}inskas & Nadja Flechner & Jingyu Hu & Philip Tetlock & Ezra Karger, 2026, "Measuring Judgment Quality in Natural-Language Explanations: Evidence from Forecasting Tournaments," Papers, arXiv.org, number 2606.30987, Jun.
• Alam, M. Jahangir & Boyle, Shane & Li, Huiyu & Sekhposyan, Tatevik, 2026, "ChatMacro: Evaluating Inflation Forecasts of Generative AI," CEPR Discussion Papers, Centre for Economic Policy Research, number 21057, Jan.
• Hannes Wallimann & C'edric Brutsch & Martin Huber, 2026, "Visible or Covert? The Causal Effect of Inspector Visibility on Fare Evasion Detection: A Causal Machine Learning and Policy Learning Approach," Papers, arXiv.org, number 2606.24181, Jun, revised Jul 2026.
• Andreas Ferrara, 2026, "A Practitioner's Guide to Using Large Language Models and Generative AI in Economic History," NBER Working Papers, National Bureau of Economic Research, Inc, number 35374, Jun.
• Shujie Li & Yuanhua Feng, 2026, "Forecasting economic growth with traditional methods and a simple neural network model," Working Papers CIE, Paderborn University, CIE Center for International Economics, number 172, Mar.
• Born, Benjamin & Lamersdorf, Nora & Schuster, Jana-Lynn & Steffen, Sascha, 2025, "From Tweets to Transactions: High-Frequency Inflation Expectations, Consumption, and Stock Returns," CEPR Discussion Papers, Centre for Economic Policy Research, number 20977, Dec.
• Paul X. McCarthy & Rasika Amarasiri & Xian Gong, 2026, "Translation Readiness Index: Measuring the Semantic Proximity of Research to Patented Science," Papers, arXiv.org, number 2606.31102, Jun, revised Aug 2026.
• Sergio A. Correia & Stephan Luck & Emil Verner, 2026, "Using AI to Let History Speak About Bank Runs," Liberty Street Economics, Federal Reserve Bank of New York, number 20260707a, Jul, DOI: 10.59576/lse.20260707a.
• Masood Tadi & Milan Fičura & and Jiří Witzany, 2026, "Natural Gas Storage Valuation Using Deep Reinforcement Learning," FFA Working Papers, Prague University of Economics and Business, number 6.003, Jun, revised 12 Jun 2026.
• Ziwen Zu, 2026, "Talking Politics with Artificial Intelligence," Papers, arXiv.org, number 2607.00551, Jul, revised Jul 2026.
• Kwon, Byeungchun & Park, Taejin & Rungcharoenkitkul, Phurichai & Smets, Frank, 2025, "Parsing the Pulse: Decomposing Macroeconomic Sentiment with LLMs," CEPR Discussion Papers, Centre for Economic Policy Research, number 20828, Nov.
• Cosmin Borsa & Michael Ludkovski, 2026, "Continuous-time Optimal Stopping through Deep Reinforcement Learning," Papers, arXiv.org, number 2606.17545, Jun.
• Riboni, Alessandro & Ruge-Murcia, Francisco & Tran, Linh, 2025, "Deliberation and Policy Outcomes: Evidence from the Textual Analysis of FOMC Transcripts," CEPR Discussion Papers, Centre for Economic Policy Research, number 20840, Nov.
• Foltyn, Richard & Olsson, Jonna, 2026, "The Worth of a "Wo": Gender Bias in Financial Advice from LLMs," CEPR Discussion Papers, Centre for Economic Policy Research, number 21323, Mar.
• Gorin, Clement & Combes, Pierre-Philippe & Duranton, Gilles & Gobillon, Laurent, 2025, "Measuring Land Use Changes by (Machine) Learning from Historical Maps," CEPR Discussion Papers, Centre for Economic Policy Research, number 20946, Dec.
• Tianjia Dong & Nadav Kunievsky & James A. Evans, 2026, "Measuring Behavior Portability in Large Language Models," Papers, arXiv.org, number 2606.22797, Jun.
• Matthieu Bunel & Elisabeth Tovar & Marie-Noëlle Lefebvre, 2026, "The Customer Is Always Right? How LLMs and Humans Judge Discrimination," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2026-16.