Report NEP-BIG-2024-09-23

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:

• Tom Coupé, 2024. "Revealed Preferences: ChatGPT’s Opinion on Economic Issues and the Economics Profession," Working Papers in Economics 24/13, University of Canterbury, Department of Economics and Finance.
• Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
• Tarek A. Hassan & Stephan Hollander & Aakash Kalyani & Markus Schwedeler & Ahmed Tahoun & Laurence van Lent, 2024. "Economic Surveillance using Corporate Text," Working Papers 2024-022, Federal Reserve Bank of St. Louis.
• Lucas Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org.
• Domenico Moramarco & Paolo Brunori & Pedro Salas-Rojo, 2024. "Biases in inequality of opportunity estimates: measures and solutions," Working Papers 675, ECINEQ, Society for the Study of Economic Inequality.
• Sina Montazeri & Haseebullah Jumakhan & Sonia Abrasiabian & Amir Mirzaeinia, 2024. "Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learning," Papers 2408.11859, arXiv.org.
• Sid Bhatia & Sidharth Peri & Sam Friedman & Michelle Malen, 2024. "High-Frequency Trading Liquidity Analysis | Application of Machine Learning Classification," Papers 2408.10016, arXiv.org.
• Lijuan Wang & Yijia Hu & Yan Zhou, 2024. "Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis," Papers 2408.12115, arXiv.org.
• Daniel Souza & Aldo Geuna & Jeff Rodr'iguez, 2024. "How Small is Big Enough? Open Labeled Datasets and the Development of Deep Learning," Papers 2408.10359, arXiv.org.
• Yuntao Wu & Jiayuan Guo & Goutham Gopalakrishna & Zisis Poulos, 2024. "Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models," Papers 2408.10368, arXiv.org, revised Oct 2024.
• Soren Bettels & Stefan Weber, 2024. "An Integrated Approach to Importance Sampling and Machine Learning for Efficient Monte Carlo Estimation of Distortion Risk Measures in Black Box Models," Papers 2408.02401, arXiv.org.
• Stephan Hetzenecker & Maximilian Osterhaus, 2024. "Deep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models," Papers 2408.09560, arXiv.org.
• Zheng Cao, 2024. "Stochastic Calculus for Option Pricing with Convex Duality, Logistic Model, and Numerical Examination," Papers 2408.05672, arXiv.org.
• Domenico Moramarco & Paolo Brunori & Pedro Salas-Rojo, 2024. "Biases in inequality of opportunity estimates: measures and solutions," SERIES 02-2024, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Aug 2024.
• Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024. "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers 2408.09598, arXiv.org, revised Sep 2024.
• Qianqian Xie & Dong Li & Mengxi Xiao & Zihao Jiang & Ruoyu Xiang & Xiao Zhang & Zhengyu Chen & Yueru He & Weiguang Han & Yuzhe Yang & Shunian Chen & Yifei Zhang & Lihang Shen & Daniel Kim & Zhiwei Liu, 2024. "Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications," Papers 2408.11878, arXiv.org.
• Federico Daniel Forte, 2024. "Argentina | Pronóstico de inflación de corto plazo con modelos Random Forest [Argentina | Forecasting short-term inflation with Random Forest Models]," Working Papers 24/10, BBVA Bank, Economic Research Department.
• CJ Finnegan & James F. McCann & Salissou Moutari, 2024. "Less is more: AI Decision-Making using Dynamic Deep Neural Networks for Short-Term Stock Index Prediction," Papers 2408.11740, arXiv.org.
• Simon D Angus & Lachlan O'Neill, 2024. "Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs," Papers 2408.09742, arXiv.org.
• Zitian Gao & Yihao Xiao, 2024. "Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method," Papers 2408.09420, arXiv.org, revised Aug 2024.
• Julia M. Puaschunder, 2024. "Knowledge in the 21st Century: Making Sense of Big Data," RAIS Conference Proceedings 2022-2024 0386, Research Association for Interdisciplinary Studies.
• Item repec:hal:journl:hal-04178278 is not listed on IDEAS anymore

• Jianqing Fan & Weining Wang & Yue Zhao, 2024. "Conditional nonparametric variable screening by neural factor regression," Papers 2408.10825, arXiv.org.
• Julia Schmidt & Graham Pilgrim & Annabelle Mourougane, 2024. "Measuring the demand for AI skills in the United Kingdom," OECD Artificial Intelligence Papers 25, OECD Publishing.
• Aleksandar Arandjelovi'c & Julia Eisenberg, 2024. "Reinsurance with neural networks," Papers 2408.06168, arXiv.org.
• Aleksandar Arandjelovi'c & Pavel V. Shevchenko & Tomoko Matsui & Daisuke Murakami & Tor A. Myrvoll, 2024. "Solving stochastic climate-economy models: A deep least-squares Monte Carlo approach," Papers 2408.09642, arXiv.org.
• Julia M. Puaschunder, 2024. "Big Data Inequality," RAIS Conference Proceedings 2022-2024 0415, Research Association for Interdisciplinary Studies.
• Item repec:hal:journl:hal-04670309 is not listed on IDEAS anymore

• Nicolás Forteza & Elvira Prades & Marc Roca, 2024. "Analysing the VAT Cut Pass-Through in Spain Using Web Scraped Supermarket Data and Machine Learning," Working papers 951, Banque de France.