Report NEP-BIG-2023-04-03

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:

• Konstantin Boss & Andre Groeger & Tobias Heidland & Finja Krueger & Conghan Zheng, 2023. "Forecasting Bilateral Refugee Flows with High-dimensional Data and Machine Learning Techniques," Working Papers 1387, Barcelona School of Economics.
• Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
• Aggarwal, Sakshi, 2023. "Machine Learning algorithms, perspectives, and real-world application: Empirical evidence from United States trade data," MPRA Paper 116579, University Library of Munich, Germany.
• Dylan Brewer & Alyssa Carlson, 2023. "Addressing Sample Selection Bias for Machine Learning Methods," Working Papers 2302, Department of Economics, University of Missouri.
• Norbäck, Pehr-Johan & Persson, Lars, 2023. "Why Big Data Can Make Creative Destruction More Creative – But Less Destructive," Working Paper Series 1454, Research Institute of Industrial Economics.
• Raffaele De Marchi & Alessandro Moro, 2023. "Forecasting fiscal crises in emerging markets and low-income countries with machine learning models," Temi di discussione (Economic working papers) 1405, Bank of Italy, Economic Research and International Relations Area.
• Muhammad Hamza Amjad, 2023. "Artificial Intelligence (AI) and Policy in Developing Countries," PIDE Webinar Brief 2023:115, Pakistan Institute of Development Economics.
• Gordon Burtch & Edward McFowland III & Mochen Yang & Gediminas Adomavicius, 2023. "EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference," Papers 2303.02820, arXiv.org, revised Dec 2024.
• Mr. Jorge A Chan-Lau & Ruofei Hu & Maksym Ivanyna & Ritong Qu & Cheng Zhong, 2023. "Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models," IMF Working Papers 2023/041, International Monetary Fund.
• Anastasis Kratsios & Cody Hyndman, 2023. "Generative Ornstein-Uhlenbeck Markets via Geometric Deep Learning," Papers 2302.09176, arXiv.org.
• Luca Badolato & Ari Gabriel Decter-Frain & Nicolas Irons & Maria Laura Miranda & Erin Walk & Elnura Zhalieva & Monica J. Alexander & Ugofilippo Basellini & Emilio Zagheni, 2023. "The limits of predicting individual-level longevity," MPIDR Working Papers WP-2023-008, Max Planck Institute for Demographic Research, Rostock, Germany.
• Seoyun Hong, 2023. "Censored Quantile Regression with Many Controls," Papers 2303.02784, arXiv.org.
• Tohid Atashbar & Rui Aruhan Shi, 2023. "AI and Macroeconomic Modeling: Deep Reinforcement Learning in an RBC model," IMF Working Papers 2023/040, International Monetary Fund.
• Lett, Elle & La Cava, William, 2023. "Translating Intersectionality to Fair Machine Learning in Health Sciences," SocArXiv gu7yh, Center for Open Science.
• Yuan Gao & Biao Jiang & Jietong Zhou, 2023. "Financial Distress Prediction For Small And Medium Enterprises Using Machine Learning Techniques," Papers 2302.12118, arXiv.org.
• Sobin Joseph & Shashi Jain, 2023. "A neural network based model for multi-dimensional nonlinear Hawkes processes," Papers 2303.03073, arXiv.org.
• Mohamed Hamdouche & Pierre Henry-Labordere & Huyen Pham, 2023. "Policy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing," Papers 2302.07320, arXiv.org.
• Sturm, Timo, 2023. "Exploring Human and Artificial Intelligence Collaboration and Its Impact on Organizational Performance: A Multi-Level Analysis," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 137083, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Sam Dannels, 2023. "Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data," Papers 2302.10490, arXiv.org.
• Aldo Glielmo & Marco Favorito & Debmallya Chanda & Domenico Delli Gatti, 2023. "Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs," Papers 2302.11835, arXiv.org, revised Dec 2023.
• Peter Egger & Susie Xi Rao & Sebastiano Papini, 2023. "Building Floorspace in China: A Dataset and Learning Pipeline," Papers 2303.02230, arXiv.org, revised Jun 2023.
• Rob Bauer & Dirk Broeders & Annick van Ool, 2023. "Walk the green talk? A textual analysis of pension funds’ disclosures of sustainable investing," Working Papers 770, DNB.
• Paolo Bova & Alessandro Di Stefano & The Anh Han, 2023. "Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety," Papers 2303.03174, arXiv.org.