Report NEP-BIG-2025-05-19

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé 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:

• Ziqi Li & Zhan Peng, 2025. "Can Moran Eigenvectors Improve Machine Learning of Spatial Data? Insights from Synthetic Data Validation," Papers 2504.12450, arXiv.org.
• Hannes Wallimann & Noah Balthasar, 2025. "Predicting Children's Travel Modes for School Journeys in Switzerland: A Machine Learning Approach Using National Census Data," Papers 2504.09947, arXiv.org.
• Yu Zhang & Zelin Wu & Claudio Tessone, 2025. "Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks," Papers 2504.12771, arXiv.org.
• Kasymkhan Khubiev & Mikhail Semenov, 2025. "Deep Learning Models Meet Financial Data Modalities," Papers 2504.13521, arXiv.org, revised Apr 2025.
• Anton Yang & Jianwei Ai & Costas Arkolakis, 2025. "A Geospatial Approach to Measuring Economic Activity," NBER Working Papers 33619, National Bureau of Economic Research, Inc.
• Koukorinis, Andreas & Peters, Gareth W. & Germano, Guido, 2025. "Generative-discriminative machine learning models for high-frequency financial regime classification," LSE Research Online Documents on Economics 128016, London School of Economics and Political Science, LSE Library.
• Zongxiao Wu & Yizhe Dong & Yaoyiran Li & Baofeng Shi, 2025. "Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts," Papers 2503.18029, arXiv.org.
• Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2025. "MLOps Monitoring at Scale for Digital Platforms," Papers 2504.16789, arXiv.org.
• Martina Halouskov'a & v{S}tefan Ly'ocsa, 2025. "Forecasting U.S. equity market volatility with attention and sentiment to the economy," Papers 2503.19767, arXiv.org.
• Achim Ahrens & Victor Chernozhukov & Christian Hansen & Damian Kozbur & Mark Schaffer & Thomas Wiemann, 2025. "An Introduction to Double/Debiased Machine Learning," Papers 2504.08324, arXiv.org.
• Timoth'ee Fabre & Damien Challet, 2025. "Learning the Spoofability of Limit Order Books With Interpretable Probabilistic Neural Networks," Papers 2504.15908, arXiv.org.
• Julian Junyan Wang & Victor Xiaoqi Wang, 2025. "Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks," Papers 2503.16974, arXiv.org, revised Mar 2025.
• Chris Hays & Manish Raghavan, 2025. "Double Machine Learning for Causal Inference under Shared-State Interference," Papers 2504.08836, arXiv.org.
• Tianshi Mu & Pranjal Rawat & John Rust & Chengjun Zhang & Qixuan Zhong, 2025. "Who is More Bayesian: Humans or ChatGPT?," Papers 2504.10636, arXiv.org.
• Zhao, Chuqing & Chen, Yisong, 2025. "LLM-powered Topic Modeling for Discovering Public Mental Health Trends in Social Media," SocArXiv xbpts_v1, Center for Open Science.
• Tianhe Zhang & Suhan Liu & Peng Shi, 2025. "Discrimination-free Insurance Pricing with Privatized Sensitive Attributes," Papers 2504.11775, arXiv.org.
• Julien Pascal, 2025. "Solving economic models with neural networks without backpropagation," BCL working papers 196, Central Bank of Luxembourg.
• Cong William Lin & Wu Zhu, 2025. "Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing," Papers 2504.13629, arXiv.org.
• Herbert Dawid & Philipp Harting & Hankui Wang & Zhongli Wang & Jiachen Yi, 2025. "Agentic Workflows for Economic Research: Design and Implementation," Papers 2504.09736, arXiv.org.