Report NEP-CMP-2026-07-20

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-CMP

The following items were announced in this report:

• Cosmin Borsa & Michael Ludkovski, 2026, "Continuous-time Optimal Stopping through Deep Reinforcement Learning," Papers, arXiv.org, number 2606.17545, 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.
• Manon Reusens & Sofie Goethals & David Martens, 2026, "LLM Consumer Behavior Theory: Foundations of a Novel Research Field," Papers, arXiv.org, number 2606.18005, Jun.
• 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.
• 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.
• Dirk Bergemann & Soheil Ghili & Xinyang Hu & Chuanhao Li & Zhuoran Yang, 2026, "Training Language Models for Bilateral Trade with Private Information," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2514, Apr.
• Yang, Yucheng & Wang, Chiyuan & Schaab, Andreas & Moll, Benjamin, 2025, "Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics," CEPR Discussion Papers, Centre for Economic Policy Research, number 20980, Dec.
• Ziwen Zu, 2026, "Talking Politics with Artificial Intelligence," Papers, arXiv.org, number 2607.00551, Jul, revised Jul 2026.
• Gu, Gyun Cheol, 2026, "Is AI Becoming More Human? Evidence from LLMs and the Ultimatum Game," MPRA Paper, University Library of Munich, Germany, number 129505, Jun.
• Isidro Moroso Varona & Jakub Micha'nk'ow & Pawe{l} Sakowski, 2026, "Randomized Neural Networks for estimation of exposure profiles and Credit Valuation Adjustment (CVA) for American Equity Options," Papers, arXiv.org, number 2606.24309, Jun, revised Jul 2026.
• Shakya Jayakody & Prarthinie Jayakody, 2026, "KineticSim: A Lightweight, High-Performance Execution Engine for Real-Time Market Simulators," Papers, arXiv.org, number 2606.21784, Jun, revised Jun 2026.
• 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.
• 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.
• Tianjia Dong & Nadav Kunievsky & James A. Evans, 2026, "Measuring Behavior Portability in Large Language Models," Papers, arXiv.org, number 2606.22797, Jun.
• 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.
• 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.
• 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.
• Thijs van den Berg, 2026, "Fast, Reliable, and Error-Bounded Option Pricing with Pretrained Neural Networks: A GJR--GARCH Study," Papers, arXiv.org, number 2606.15502, Jun.
• Lin Liu & Rajarshi Mukherjee & James M Robins, 2026, "On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models," Papers, arXiv.org, number 2606.22391, Jun, revised Sep 2026.
• Emre Yusuf & Ren Takahashi & Jayabrata Bhaduri, 2026, "PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation," Papers, arXiv.org, number 2606.15452, Jun.
• Sebastian Jensen & Siem Jan Koopman, 2026, "Neural networks for nonlinear regression with serially correlated disturbances: Evidence from cloud cover," Papers, arXiv.org, number 2606.22483, Jun, revised Aug 2026.
• Sebastien Lleo & Wolfgang Runggaldier, 2026, "Reinforcement Learning for Risk-Sensitive Investment Management: a Free Energy--Entropy Duality Approach," Papers, arXiv.org, number 2606.20903, Jun.
• Daniele Maria Di Nosse & Fabrizio Lillo, 2026, "Mitigating Adverse Selection in Concentrated Liquidity AMMs with Dynamic Fees: An Agent-Based Model Approach," Papers, arXiv.org, number 2606.23070, Jun.
• Jan H. R. Dressler & Peter Kurz & Winfried J. Steiner, 2026, "Discrete Choice and Competitive Reactions: End-to-End Simulation with the R Package cash," Papers, arXiv.org, number 2606.15593, Jun.
• Quanyan Zhu, 2026, "Agentomics: Economic Foundations for the Valuation, Attribution, and Pricing of AI Agents in Human-AI Workflows," Papers, arXiv.org, number 2606.14769, Jun.
• Likai Chen & Weining Wang, 2026, "From Vector Autoregressions to AI-based Time Series Forecasting: A Review," Bristol Economics Discussion Papers, School of Economics, University of Bristol, UK, number 26/838, 01.

Report NEP-CMP-2026-07-20

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-CMP

The following items were announced in this report: