Report NEP-BIG-2023-11-20

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.

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

Other reports in NEP-BIG

The following items were announced in this report:

• Nino Paulus & Lukas Lautenschlaeger & Wolfgang Schäfers, 2023. "Social Media and Real Estate: Do Twitter users predict REIT performance?," ERES eres2023_200, European Real Estate Society (ERES).
• Ioannis Nasios & Konstantinos Vogklis, 2023. "Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series," Papers 2310.13029, arXiv.org.
• Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
• Juan Tenorio & Wilder Pérez, 2023. "GDP nowcasting with Machine Learning and Unstructured Data to Peru," Working Papers 197, Peruvian Economic Association.
• Xin Du & Kai Moriyama & Kumiko Tanaka-Ishii, 2023. "Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation," Papers 2310.14536, arXiv.org.
• Jakob Kozak & Maximilian Nagl & Cathrine Nagl & Eli Beracha & Wolfgang Schäfers, 2023. "Determinants of U.S. REIT Bond Risk Premia with Explainable Machine Learning," ERES eres2023_146, European Real Estate Society (ERES).
• Nicolás Forteza & Sandra García-Uribe, 2023. "A Score Function to Prioritize Editing in Household Survey Data: A Machine Learning Approach," Working Papers 2330, Banco de España.
• Giovanni Compiani & Ilya Morozov & Stephan Seiler, 2023. "Demand Estimation with Text and Image Data," CESifo Working Paper Series 10695, CESifo.
• Sebastian Heinrich, 2023. "Deriving Technology Indicators from Corporate Websites: A Comparative Assessment Using Patents," KOF Working papers 22-512, KOF Swiss Economic Institute, ETH Zurich.
• Edson Pindza & Jules Clement Mba & Sutene Mwambi & Nneka Umeorah, 2023. "Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model," Papers 2310.09622, arXiv.org.
• Julia Hatamyar & Noemi Kreif & Rudi Rocha & Martin Huber, 2023. "Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity," Papers 2310.11962, arXiv.org.
• Thomas R. Cook & Nathan M. Palmer, 2023. "Understanding Models and Model Bias with Gaussian Processes," Research Working Paper RWP 23-07, Federal Reserve Bank of Kansas City.
• Pan Zhao & Yifan Cui, 2023. "A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning," Papers 2310.09545, arXiv.org.
• Kieran Wood & Samuel Kessler & Stephen J. Roberts & Stefan Zohren, 2023. "Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies," Papers 2310.10500, arXiv.org, revised Mar 2024.
• Piotr Pomorski & Denise Gorse, 2023. "Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes," Papers 2310.04536, arXiv.org.
• Yong Bian & Xiqian Wang & Qin Zhang, 2023. "How Does China's Household Portfolio Selection Vary with Financial Inclusion?," Papers 2311.01206, arXiv.org.
• Ghislain Geniaux, 2023. "Functional gradient descent boosting for additive non‐linear spatial autoregressive model (gaussian and probit)," Post-Print hal-04229868, HAL.
• Bhaskarjit Sarmah & Tianjie Zhu & Dhagash Mehta & Stefano Pasquali, 2023. "Towards reducing hallucination in extracting information from financial reports using Large Language Models," Papers 2310.10760, arXiv.org.
• Silvia Albrizio & Allan Dizioli & Pedro Vitale Simon, 2023. "Mining the Gap: Extracting Firms’ Inflation Expectations From Earnings Calls," IMF Working Papers 2023/202, International Monetary Fund.
• Borowiecki, Karol Jan & Pedersen, Maja U. & Mitchell, Sara Beth, 2023. "Using big data to measure cultural tourism in Europe with unprecedented precision," Discussion Papers on Economics 5/2023, University of Southern Denmark, Department of Economics.
• Jann Spiess & Guido Imbens & Amar Venugopal, 2023. "Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control," NBER Working Papers 31802, National Bureau of Economic Research, Inc.
• Joshua Rosaler & Dhruv Desai & Bhaskarjit Sarmah & Dimitrios Vamvourellis & Deran Onay & Dhagash Mehta & Stefano Pasquali, 2023. "Enhanced Local Explainability and Trust Scores with Random Forest Proximities," Papers 2310.12428, arXiv.org, revised Aug 2024.
• George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.