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 (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:

• Nino Paulus & Lukas Lautenschlaeger & Wolfgang Schäfers, 2023, "Social Media and Real Estate: Do Twitter users predict REIT performance?," ERES, European Real Estate Society (ERES), number eres2023_200, Jan.
• Ioannis Nasios & Konstantinos Vogklis, 2023, "Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series," Papers, arXiv.org, number 2310.13029, Oct.
• Patrick Rehill & Nicholas Biddle, 2023, "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers, arXiv.org, number 2310.13240, Oct, revised Mar 2024.
• Juan Tenorio & Wilder Pérez, 2023, "GDP nowcasting with Machine Learning and Unstructured Data to Peru," Working Papers, Peruvian Economic Association, number 197, Nov.
• Xin Du & Kai Moriyama & Kumiko Tanaka-Ishii, 2023, "Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation," Papers, arXiv.org, number 2310.14536, Oct.
• Jakob Kozak & Maximilian Nagl & Cathrine Nagl & Eli Beracha & Wolfgang Schäfers, 2023, "Determinants of U.S. REIT Bond Risk Premia with Explainable Machine Learning," ERES, European Real Estate Society (ERES), number eres2023_146, Jan.
• Nicolás Forteza & Sandra García-Uribe, 2023, "A Score Function to Prioritize Editing in Household Survey Data: A Machine Learning Approach," Working Papers, Banco de España, number 2330, Oct, DOI: https://doi.org/10.53479/34613.
• Item repec:ces:ceswps:_10695 is not listed on IDEAS anymore

• Sebastian Heinrich, 2023, "Deriving Technology Indicators from Corporate Websites: A Comparative Assessment Using Patents," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 22-512, Jul, DOI: 10.3929/ethz-b-000623739.
• Edson Pindza & Jules Clement Mba & Sutene Mwambi & Nneka Umeorah, 2023, "Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model," Papers, arXiv.org, number 2310.09622, Oct.
• Julia Hatamyar & Noemi Kreif & Rudi Rocha & Martin Huber, 2023, "Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity," Papers, arXiv.org, number 2310.11962, Oct.
• Thomas R. Cook & Nathan M. Palmer, 2023, "Understanding Models and Model Bias with Gaussian Processes," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 23-07, Jun, DOI: 10.18651/RWP2023-07.
• Pan Zhao & Yifan Cui, 2023, "A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning," Papers, arXiv.org, number 2310.09545, Oct.
• Kieran Wood & Samuel Kessler & Stephen J. Roberts & Stefan Zohren, 2023, "Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies," Papers, arXiv.org, number 2310.10500, Oct, revised Mar 2024.
• Piotr Pomorski & Denise Gorse, 2023, "Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes," Papers, arXiv.org, number 2310.04536, Sep.
• Yong Bian & Xiqian Wang & Qin Zhang, 2023, "How Does China's Household Portfolio Selection Vary with Financial Inclusion?," Papers, arXiv.org, number 2311.01206, Nov.
• Ghislain Geniaux, 2023, "Functional gradient descent boosting for additive non‐linear spatial autoregressive model (gaussian and probit)," Post-Print, HAL, number hal-04229868, May.
• Bhaskarjit Sarmah & Tianjie Zhu & Dhagash Mehta & Stefano Pasquali, 2023, "Towards reducing hallucination in extracting information from financial reports using Large Language Models," Papers, arXiv.org, number 2310.10760, Oct.
• Silvia Albrizio & Allan Dizioli & Pedro Vitale Simon, 2023, "Mining the Gap: Extracting Firms’ Inflation Expectations From Earnings Calls," IMF Working Papers, International Monetary Fund, number 2023/202, Oct.
• 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, University of Southern Denmark, Department of Economics, number 5/2023, Nov.
• 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, National Bureau of Economic Research, Inc, number 31802, Oct.
• 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, arXiv.org, number 2310.12428, Oct, revised Aug 2024.
• George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023, "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 8/23.

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 (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: