Report NEP-ETS-2026-06-22

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:

• Marie Corillon & Stephan Smeekes & Ines Wilms, 2026, "Sparse Tree-Based Aggregation for Time Series Regressions," Papers, arXiv.org, number 2606.03665, Jun.
• Lei Jia & Shouri Hu & Zhaoxing Gao, 2026, "Structural Change Detection in High-Dimensional Transformed Factor Models via Canonical Correlation Analysis," Papers, arXiv.org, number 2606.01553, Jun.
• Hyeon-seung Huh & David Kim, 2026, "Exact identification, robust inference, and shock masquerading in sign-restricted SVARs," Working papers, Yonsei University, Yonsei Economics Research Institute, number 2026rwp-293, Jun.
• del Barrio Castro, Tomás & Escribano, Álvaro & Özer, Yeliz & Sibbertsen Philipp, 2026, "Frequency-Specific Coupling in Cenozoic Climate Variability," Hannover Economic Papers (HEP), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, number dp-749, Jun.
• Saakstra, Sake, 2026, "A Time-Varying-Parameter State-Space Approach to Sparse-Event Survival Modelling: Methodological Design, Out-of-Sample Performance, and Application to Hydrogen Project Implementation-Risk," MPRA Paper, University Library of Munich, Germany, number 129308, May.
• Jan Rovirosa & Jesse Schmolze, 2026, "Inspectable Neural Markov Models for Non-Stationary Time Series," Papers, arXiv.org, number 2605.30943, May.
• Xinyue Fang & Robert 'Slepaczuk, 2026, "Volatility Forecasting and Return Prediction under Market Regimes: Evidence from High-Frequency Chinese Equity Data," Papers, arXiv.org, number 2606.09478, Jun.
• Vasnev, Andrey & Liu, Chu-An, 2026, "Corrected Forecast Combinations," Working Papers, University of Sydney Business School, Discipline of Business Analytics, number BAWP-2026-01, Jan.
• Konrad J. Mueller & Nikita Zozoulenko & Ben Wood & Thomas Cass & Lukas Gonon, 2026, "Generating Financial Time Series by Matching Random Convolutional Features," Papers, arXiv.org, number 2606.05138, Jun.
• Mingxuan Yi & Vidal Mehra & Jing Chen & John Cartlidge, 2026, "Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market," Papers, arXiv.org, number 2605.30363, May.
• Akash Deep & Nicholas Appiah & Svetlozar T. Rachev, 2026, "Memory, Roughness, and Information Persistence in Financial Markets: A Structural Approach to Volatility Forecasting," Papers, arXiv.org, number 2605.24285, May.
• Aoxin Zhang & Yuhan Cheng & Kwanting Leung, 2026, "Benchmarking Deep Time Series Models for Equity Portfolios," Papers, arXiv.org, number 2606.09420, Jun.
• Daniel Cunha Oliveira & Kieran Wood & Stefan Zohren & Mihai Cucuringu & Andr'e Fujita, 2026, "Macro-aware time series forecasting via hierarchical mixed-frequency attention models," Papers, arXiv.org, number 2606.00624, May.