Report NEP-FOR-2025-12-15

This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Malte Knüppel issued this report. It is usually issued weekly.

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Other reports in NEP-FOR

The following items were announced in this report:

• Boge Lyu & Qianye Yin & Iris Denise Tommelein & Hanyang Liu & Karnamohit Ranka & Karthik Yeluripati & Junzhe Shi, 2025. "A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches," Papers 2512.09360, arXiv.org.
• Eden Gross & Ryan Kruger & Francois Toerien, 2025. "Standard and stressed value at risk forecasting using dynamic Bayesian networks," Papers 2512.05661, arXiv.org.
• A. L. Paredes, 2025. "A3T-GCN for FTSE100 Components Price Forecasting," Papers 2511.21873, arXiv.org.
• Liang, Weifang & Liu, Yong & Somogyi, Simon & Anderson, David P., 2024. "A Multi-Model, Ensemble Approach to Forecasting United States Food Prices," 2024 Annual Meeting, July 28-30, New Orleans, LA 343687, Agricultural and Applied Economics Association.
• Xiang Gao & Cody Hyndman, 2025. "Arbitrage-Free Bond and Yield Curve Forecasting with Neural Filters under HJM Constraints," Papers 2511.17892, arXiv.org.
• Eghbal Rahimikia & Hao Ni & Weiguan Wang, 2025. "Re(Visiting) Time Series Foundation Models in Finance," Papers 2511.18578, arXiv.org.
• Shilong Han, 2025. "The Effect of High-Speed Rail Connectivity on Capital Market Earnings Forecast Error: Evidence from the Chinese Stock Market," Papers 2512.03709, arXiv.org.
• Luca Attolico, 2025. "Opening the Black Box: Nowcasting Singapore's GDP Growth and its Explainability," Papers 2512.02092, arXiv.org.
• Frederik Rech & Fanchen Meng & Hussam Musa & Martin v{S}ebev{n}a & Siele Jean Tuo, 2025. "Does Firm-Level AI Adoption Improve Early-Warning of Corporate Financial Distress? Evidence from Chinese Non-Financial Firms," Papers 2512.02510, arXiv.org.
• Brian Ezinwoke & Oliver Rhodes, 2025. "Predicting Price Movements in High-Frequency Financial Data with Spiking Neural Networks," Papers 2512.05868, arXiv.org.
• Luca Attolico, 2025. "Explainable Machine Learning for Macroeconomic and Financial Nowcasting: A Decision-Grade Framework for Business and Policy," Papers 2512.00399, arXiv.org.
• Jun Kevin & Pujianto Yugopuspito, 2025. "Hybrid LSTM and PPO Networks for Dynamic Portfolio Optimization," Papers 2511.17963, arXiv.org.
• Drin, Svitlana & Avdieienko, Ivan & Chornei, Ruslan, 2025. "Iterative Demand Optimization Using the Discrete Functional Particle Method," Working Papers 2025:17, Örebro University, School of Business.
• Angelini, Elena & Darracq Pariès, Matthieu & Haertel, Thomas & Lalik, Magdalena & Aldama, Pierre & Brázdik, František & Damjanović, Milan & Fantino, Davide & Sanchez, Pablo Garcia & Guarda, Paolo & Ke, 2025. "The ESCB forecasting models: what are they and what are they good for?," Occasional Paper Series 381, European Central Bank.
• Bagattini Alexander & Chen Shao, 2025. "Prejudiced Futures? Algorithmic Bias in Time Series Forecasting and Its Ethical Implications," Papers 2512.01877, arXiv.org.
• Klakow Akepanidtaworn & Korkrid Akepanidtaworn, 2025. "GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies," IMF Working Papers 2025/252, International Monetary Fund.
• McWilliams, William N. & Isengildina Massa, Olga & Stewart, Shamar L., 2024. "Annual Food Price Inflation Forecasting: A Macroeconomic Random Forest Approach," 2024 Annual Meeting, July 28-30, New Orleans, LA 343923, Agricultural and Applied Economics Association.
• Andersson, Jonas & Karlis, Dimitris, 2025. "Maximum Likelihood Estimation of the Vector AutoRegressive To Anything (VARTA) model," Discussion Papers 2025/25, Norwegian School of Economics, Department of Business and Management Science.
• Tarek Jouini, 2025. "Consistent boundaries for the one-step-ahead forecast error criterion and the AIC in vector autoregressions," Working Papers 2506, University of Windsor, Department of Economics.