Report NEP-FOR-2026-02-02

This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Rob J Hyndman 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:

• Heinisch, Katja & van Norden, Simon & Wildi, Marc, 2026, "Smooth and persistent forecasts of German GDP: Balancing accuracy and stability," IWH Discussion Papers, Halle Institute for Economic Research (IWH), number 1/2026, DOI: 10.18717/dp99kr-7336.
• Minh Nguyen & Farshid Vahid & Shanika L. Wickramasuriya, 2025, "Hierarchical Forecasting: The Role of Information," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 11/25.
• Le Wang & Boyuan Zhang, 2026, "The Promise of Time-Series Foundation Models for Agricultural Forecasting: Evidence from Commodity Prices," Papers, arXiv.org, number 2601.06371, Jan, revised Jan 2026.
• Mohammed Alruqimi & Luca Di Persio, 2025, "Integrating LSTM Networks with Neural Levy Processes for Financial Forecasting," Papers, arXiv.org, number 2512.07860, Nov.
• Fredy Pokou & Jules Sadefo Kamdem & Kpante Emmanuel Gnandi, 2026, "Predictive Accuracy versus Interpretability in Energy Markets: A Copula-Enhanced TVP-SVAR Analysis," Papers, arXiv.org, number 2601.19321, Jan.
• Nicolas Hardy & Dimitris Korobilis, 2025, "Learning from crises: A new class of time-varying parameter VARs with observable adaptation," Working Papers, Business School - Economics, University of Glasgow, number 2025_12, Dec.
• Ficura, Milan & Ibragimov, Rustam & Janda, Karel, 2025, "Artificial Intelligence–Based Forecasting of Oil Prices: Evidence from Neural Network Models," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 335571.
• Jinjun Liu & Ming-Yen Cheng, 2026, "Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach," Papers, arXiv.org, number 2601.04608, Jan.
• Jack Fanshawe & Rumi Masih & Alexander Cameron, 2026, "Forecasting Equity Correlations with Hybrid Transformer Graph Neural Network," Papers, arXiv.org, number 2601.04602, Jan.
• Degiannakis, Stavros & Delis, Panagiotis & Filis, George, 2025, "Forecasting household-level inflation in Greece," MPRA Paper, University Library of Munich, Germany, number 127228, Oct.
• Marc Wildi, 2026, "Sign Accuracy, Mean-Squared Error and the Rate of Zero Crossings: a Generalized Forecast Approach," Papers, arXiv.org, number 2601.06547, Jan.
• Alessio Brini & Ekaterina Seregina, 2026, "A Nonlinear Target-Factor Model with Attention Mechanism for Mixed-Frequency Data," Papers, arXiv.org, number 2601.16274, Jan.
• Alexander Eliseev, 2025, "Nowcasting Russian GDP in a mixed-frequency DSGE model with a panel of non-modelled variables," Bank of Russia Working Paper Series, Bank of Russia, number wps145, Feb.
• Craig S Wright, 2026, "Utility-Weighted Forecasting and Calibration for Risk-Adjusted Decisions under Trading Frictions," Papers, arXiv.org, number 2601.07852, Jan.
• Alexander Eliseev & Sergei Seleznev, 2026, "Fake Date Tests: Can We Trust In-sample Accuracy of LLMs in Macroeconomic Forecasting?," Papers, arXiv.org, number 2601.07992, Jan.
• Wang Yi & Takashi Hasuike, 2026, "Smart Predict--then--Optimize Paradigm for Portfolio Optimization in Real Markets," Papers, arXiv.org, number 2601.04062, Jan, revised Jan 2026.
• Anthony M. Diercks & Jared Dean Katz & Jonathan H. Wright, 2026, "Kalshi and the Rise of Macro Markets," NBER Working Papers, National Bureau of Economic Research, Inc, number 34702, Jan.
• Danila Ovechkin, 2026, "Estimation and forecasting with a Nonlinear Phillips Curve based on heterogeneous sensitivity between economic activity and CPI components," Bank of Russia Working Paper Series, Bank of Russia, number wps161, Jan.
• Zefeng Chen & Darcy Pu, 2026, "Autonomous Market Intelligence: Agentic AI Nowcasting Predicts Stock Returns," Papers, arXiv.org, number 2601.11958, Jan.