Report NEP-FOR-2025-06-09

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:

• Paponpat Taveeapiradeecharoen & Popkarn Arwatchanakarn, 2025. "Forecasting Thai inflation from univariate Bayesian regression perspective," Papers 2505.05334, arXiv.org, revised May 2025.
• Heinisch, Katja & Scaramella, Fabio & Schult, Christoph, 2025. "Assumption errors and forecast accuracy: A partial linear instrumental variable and double machine learning approach," IWH Discussion Papers 6/2025, Halle Institute for Economic Research (IWH).
• Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2024. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Post-Print hal-05056934, HAL.
• Sona Benecka, 2025. "Forecasting Disaggregated Producer Prices: A Fusion of Machine Learning and Econometric Techniques," Working Papers 2025/2, Czech National Bank, Research and Statistics Department.
• Juan Jos√© Rinc√≥n Brice√±o, 2025. "Colombian economic activity nowcasting: addressing nonlinearities and high dimensionality through machine-learning," Documentos CEDE 21388, Universidad de los Andes, Facultad de Economía, CEDE.
• Elliot Beck & Michael Wolf, 2025. "Forecasting inflation with the hedged random forest," Working Papers 2025-07, Swiss National Bank.
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2025. "Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables," Working Papers 25-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised May 2025.
• Kyungsu Kim, 2025. "Unemployment Dynamics Forecasting with Machine Learning Regression Models," Papers 2505.01933, arXiv.org.
• Lutfu Sua & Haibo Wang & Jun Huang, 2025. "Deep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Models," Papers 2505.03109, arXiv.org.
• Mohammadhossein Rashidi & Mohammad Modarres, 2025. "Predicting the Price of Gold in the Financial Markets Using Hybrid Models," Papers 2505.01402, arXiv.org.
• Nurbanu Bursa, 2025. "Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries," Papers 2506.01945, arXiv.org.
• Fourie, Jurgens & Steenkamp, Daan, 2025. "Forecasting economic downturns in South Africa using leading indicators and machine learning," MPRA Paper 124709, University Library of Munich, Germany.
• Lin, Ziqi (Rachel), 2025. "Tax share analysis and prediction of kernel extreme Learning machine optimized by vector weighted average algorithm," OSF Preprints ymjw9_v1, Center for Open Science.
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2025. "Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables," CIRANO Working Papers 2025s-15, CIRANO.
• Mert Gokcu & Eren Sezer, 2025. "Akaryakit Fiyat Olusumu: Brent Petrol Fiyati Ýyi Bir Tahmin Araci Mi?," CBT Research Notes in Economics 2507, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
• Michael Ehrmann, 2024. "Trust in Central Banks," RBA Annual Conference Papers acp2024-04, Reserve Bank of Australia, revised May 2025.
• Nikolaos Giannakis & Periklis Gogas & Theophilos Papadimitriou & Jamel Saadaoui & Emmanouil Sofianos, 2025. "Do International Reserve Holdings Still Predict Economic Crises? Insights from Recent Machine Learning Techniques," Working Papers 2025.6, International Network for Economic Research - INFER.
• Rocío Elizondo & Julio A. Carrillo, 2025. "Comparison of Inflation Expectations from Surveys and Markets Across Different Horizons," Working Papers 2025-07, Banco de México.
• Stefano Fasani & Valeria Patella & Giuseppe Pagano Giorgianni & Lorenza Rossi, 2025. "Belief Distortions and Disagreement about Inflation," Working Papers 423478673, Lancaster University Management School, Economics Department.
• Brooke Hathhorn & Michael T. Owyang, 2025. "Making Sense of Recession Probabilities," On the Economy 100021, Federal Reserve Bank of St. Louis.