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 (Tom Coupe) issued this report. It is usually issued weekly.

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

Other reports in NEP-BIG

The following items were announced in this report:

• Konstantin Boss & Finja Krueger & Conghan Zheng & Tobias Heidland & Andre Groeger, 2023, "Forecasting Bilateral Refugee Flows with High-dimensional Data and Machine Learning Techniques," Working Papers, Barcelona School of Economics, number 1387, Mar.
• Hakan Pabuccu & Adrian Barbu, 2023, "Feature Selection with Annealing for Forecasting Financial Time Series," Papers, arXiv.org, number 2303.02223, Mar, revised Feb 2024.
• Aggarwal, Sakshi, 2023, "Machine Learning algorithms, perspectives, and real-world application: Empirical evidence from United States trade data," MPRA Paper, University Library of Munich, Germany, number 116579, Mar.
• Dylan Brewer & Alyssa Carlson, 2023, "Addressing Sample Selection Bias for Machine Learning Methods," Working Papers, Department of Economics, University of Missouri, number 2302, Mar.
• Norbäck, Pehr-Johan & Persson, Lars, 2023, "Why Big Data Can Make Creative Destruction More Creative – But Less Destructive," Working Paper Series, Research Institute of Industrial Economics, number 1454, Feb.
• 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), Bank of Italy, Economic Research and International Relations Area, number 1405, Mar.
• Muhammad Hamza Amjad, 2023, "Artificial Intelligence (AI) and Policy in Developing Countries," PIDE Webinar Brief, Pakistan Institute of Development Economics, number 2023:115.
• Gordon Burtch & Edward McFowland III & Mochen Yang & Gediminas Adomavicius, 2023, "EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference," Papers, arXiv.org, number 2303.02820, Mar, 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, International Monetary Fund, number 2023/041, Feb.
• Anastasis Kratsios & Cody Hyndman, 2023, "Generative Ornstein-Uhlenbeck Markets via Geometric Deep Learning," Papers, arXiv.org, number 2302.09176, Feb.
• Luca Badolato & Ari G. Decter-Frain & Nicolas J. Irons & Maria L. Miranda & Erin Walk & Elnura Zhalieva & Monica J. Alexander & Ugofilippo Basellini & Emilio Zagheni, 2023, "The limits of predicting individual-level longevity," MPIDR Working Papers, Max Planck Institute for Demographic Research, Rostock, Germany, number WP-2023-008, DOI: 10.4054/MPIDR-WP-2023-008.
• Seoyun Hong, 2023, "Censored Quantile Regression with Many Controls," Papers, arXiv.org, number 2303.02784, Mar.
• Tohid Atashbar & Rui Aruhan Shi, 2023, "AI and Macroeconomic Modeling: Deep Reinforcement Learning in an RBC model," IMF Working Papers, International Monetary Fund, number 2023/040, Feb.
• Lett, Elle & La Cava, William, 2023, "Translating Intersectionality to Fair Machine Learning in Health Sciences," SocArXiv, Center for Open Science, number gu7yh, Feb, DOI: 10.31219/osf.io/gu7yh.
• Yuan Gao & Biao Jiang & Jietong Zhou, 2023, "Financial Distress Prediction For Small And Medium Enterprises Using Machine Learning Techniques," Papers, arXiv.org, number 2302.12118, Feb.
• Sobin Joseph & Shashi Jain, 2023, "A neural network based model for multi-dimensional nonlinear Hawkes processes," Papers, arXiv.org, number 2303.03073, Mar.
• 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, arXiv.org, number 2302.07320, Feb.
• 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), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 137083.
• Sam Dannels, 2023, "Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data," Papers, arXiv.org, number 2302.10490, Feb.
• Aldo Glielmo & Marco Favorito & Debmallya Chanda & Domenico Delli Gatti, 2023, "Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs," Papers, arXiv.org, number 2302.11835, Feb, revised Dec 2023.
• Peter Egger & Susie Xi Rao & Sebastiano Papini, 2023, "Building Floorspace in China: A Dataset and Learning Pipeline," Papers, arXiv.org, number 2303.02230, Mar, 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, DNB, number 770, Mar.
• Paolo Bova & Alessandro Di Stefano & The Anh Han, 2023, "Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety," Papers, arXiv.org, number 2303.03174, Mar.

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 (Tom Coupe) issued this report. It is usually issued weekly.

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

Other reports in NEP-BIG

The following items were announced in this report: