Report NEP-ETS-2026-10-26

This is the archive for NEP-ETS, a report on new working papers in the area of Econometric Time Series. Yong Yin issued this report. It is usually issued weekly.

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Other reports in NEP-ETS

The following items were announced in this report:

• Degui Li & Yu-Ning Li & Peter C.B. Phillips, 2026, "Large-Scale Curve Time Series with Common Stochastic Trends," Working Papers, University of Macau, Faculty of Business Administration, number 202649, Oct.
• Jiahao Weng & Jiyu Liu, 2026, "SHADE: Shrinkage via Halted Alpha-stable Denoising for Heavy-Tailed Probabilistic Time Series Forecasting," Papers, arXiv.org, number 2610.26358, Oct.
• Christian Jobelius Schulz & Christoph Hanck & Simon Hirsch & Florian Ziel, 2026, "Online Conditional Vine Copulas: Forecasting Electricity Demand," Papers, arXiv.org, number 2610.23119, Oct.
• Geert Dhaene & Arturas Juodis, 2026, "Almost Uniform Bias Correction for Persistent Panel Local Projections," Papers, arXiv.org, number 2610.26427, Oct.
• Larissa Nawo, 2026, "Financial Conditions and U.S. Macro Tail Risk," Working Papers, HEC Montreal, Canada Research Chair in Risk Management, number 26-02, Oct.
• Hongqi Chen & Chengwang Liao & Ziwei Mei & Zhentao Shi, 2026, "Cointegrated Regressors: Where Folded Concave Penalties Go Beyond LASSO," Papers, arXiv.org, number 2610.14617, Oct.
• Paulo Júlio, 2026, "Deep Learning Models in Central Bank Forecasting," Working Papers, Banco de Portugal, Economics and Research Department, number w202612.
• Ioannis Kasparis, 2026, "Conventional Inference under Nonstationary Heavy Tailed Fractional Processes & Arrays," Papers, arXiv.org, number 2610.25237, Oct.
• Tu Anh Doan Nguyen & Alessandro Bucciol, 2026, "Persistence is not Independence: A structural Credibility Protocol for Distribution based Recovery in VARLiNGAM," Papers, arXiv.org, number 2610.25238, Oct.
• Silva Lopes, Artur, 2026, "HP vs. Hamilton: which is better to estimate trends? A comparison based on Euro Area GDP," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 344078.
• Oguzhan Cepni & Rangan Gupta, 2026, "Forecasting U.S. State Real GDP Growth with Panel Machine Learning and Mixed-Frequency Data," Working Papers, University of Pretoria, Department of Economics, number 202632, Oct.
• Aadit Jerfy, 2026, "Topological Compression of Prediction Market Belief States for Event Forecasting," Papers, arXiv.org, number 2610.25756, Oct.