Report NEP-ETS-2026-08-10

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:

• Gonzalez-Casasus, Oriol & Schorfheide, Frank, 2025, "Misspecification-Robust Shrinkage and Selection for VAR Forecasts and IRFs," CEPR Discussion Papers, Centre for Economic Policy Research, number 19915, Feb.
• Mark W. Watson, 2026, "Forecasting the Covid Surge in Inflation," NBER Working Papers, National Bureau of Economic Research, Inc, number 35435, Jul.
• Nathan Schor & Minchul Shin, 2026, "ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics," Working Papers, Federal Reserve Bank of Philadelphia, number 26-38, Aug, DOI: 10.21799/frbp.wp.2026.38.
• A. Monta~n'es & E. Ruiz, 2026, "Interpreting (and testing) factor loadings," Papers, arXiv.org, number 2607.12568, Jul.
• Likai Chen & Weining Wang, 2026, "From Vector Autoregressions to AI-based Time Series Forecasting: A Review," Papers, arXiv.org, number 2607.14279, Jul.
• Sara Casella & Jesús Fernández-Villaverde & Stephen Hansen & Ryohei Oishi & Minchul Shin, 2026, "Structural Estimation with Unstructured Data," Working Papers, Federal Reserve Bank of Dallas, number 2620, Jul, DOI: 10.24149/wp2620.
• Inoue, Atsushi & Kilian, Lutz, 2025, "The Conventional Impulse Response Prior in VAR Models with Sign Restrictions," CEPR Discussion Papers, Centre for Economic Policy Research, number 20159, Apr.
• Hauzenberger, Niko & Marcellino, Massimiliano & Pfarrhofer, Michael & Stelzer, Anna, 2025, "Bayesian Nowcasting with Mixed Frequency Data Using Gaussian Processes," CEPR Discussion Papers, Centre for Economic Policy Research, number 19965, Feb.
• Jonas E. Arias & Juan F. Rubio-Ramirez & Daniel F. Waggoner, 2026, "Inference Based on Scale, Label, and Economic Restrictions," Working Papers, Federal Reserve Bank of Philadelphia, number 26-36, Jul, DOI: 10.21799/frbp.wp.2026.36.
• Chang, Jinyuan & Du, Yue & Huang, Guanglin & Yao, Qiwei, 2026, "Identification and estimation for matrix time series CP-factor models," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 130774, Jun.
• Tae-Hwy Lee & Dingli Wang, 2026, "Median-Anchored Adjustment of Joint VaR--ES Forecasts," Working Papers, University of California at Riverside, Department of Economics, number 202604, Aug.
• Sankalp Gilda, 2026, "tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series," Papers, arXiv.org, number 2607.06690, Jul.
• Xinxian Chen & Peter Reinhard Hansen & Chen Tong, 2026, "Split-Session Cluster GARCH for Overnight and Intraday Returns: The Role of Tail Heterogeneity," Papers, arXiv.org, number 2607.03669, Jul.
• Jushan Bai & Peng Wang, 2026, "Causal Inference Using Factor Models," Papers, arXiv.org, number 2606.29691, Jun.
• Daniele Angelini, 2026, "(In)Efficient Market States and Rough Volatility Detected via Grunwald-Letnikov Fractional Derivative," Papers, arXiv.org, number 2606.27932, Jun.
• O’Neill, Eoghan & Velasco, Sofia, 2026, "Let the tree decide: FABART. A non-parametric factor model for nonlinear oil shock transmission," Working Paper Series, European Central Bank, number 3265, Jul.
• Giuseppe Cavaliere & Luca Fanelli & Marco Mazzali, 2026, "Global factors for local shocks in a data-scarce environment: with an application to regional fiscal multipliers in Italy," Papers, arXiv.org, number 2607.13879, Jul.
• Anamol Khadka & Milan Arjel & Ayush Lataula & Aayam Dhakal & Prajun Trital & Mingmar Sherpa & Biman Rimal, 2026, "Modeling the Dynamic Relationship Between Brent Crude Oil Prices and the Nepal Stock Exchange: An Integrated Econometric and Explainable Machine Learning Approach," Papers, arXiv.org, number 2607.11922, Jul.
• Yurii Sholomytskyi, 2026, "Looking for Underlying Structure in WEO Forecasts," IMF Working Papers, International Monetary Fund, number 2026/164, Jul.