Report NEP-ETS-2022-12-05

This is the archive for NEP-ETS, a report on new working papers in the area of Econometric Time Series. Jaqueson K. Galimberti 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:

• Karsten Reichold, 2022. "A Residuals-Based Nonparametric Variance Ratio Test for Cointegration," Papers 2211.06288, arXiv.org, revised Dec 2022.
• Härtl, Tilmann, 2022. "Identifying Proxy VARs with Restrictions on the Forecast Error Variance," VfS Annual Conference 2022 (Basel): Big Data in Economics 264071, Verein für Socialpolitik / German Economic Association.
• Fève, Patrick & Beaudry, Paul & Collard, Fabrice & Guay, Alain & Portier, Franck, 2022. "Dynamic Identification in VARs," TSE Working Papers 22-1384, Toulouse School of Economics (TSE).
• Xiao Huang, 2022. "Boosted p-Values for High-Dimensional Vector Autoregression," Papers 2211.02215, arXiv.org, revised Mar 2023.
• Wang, Xiaohu & Yu, Jun & Zhang, Chen, 2022. "On the Optimal Forecast with the Fractional Brownian Motion," Economics and Statistics Working Papers 12-2022, Singapore Management University, School of Economics.
• Jaros{l}aw Kwapie'n & Pawel Blasiak & Stanis{l}aw Dro.zd.z & Pawe{l} O'swik{e}cimka, 2022. "Genuine multifractality in time series is due to temporal correlations," Papers 2211.00728, arXiv.org, revised Mar 2023.
• Claudia Foroni & Paolo Gelain & Massimiliano Marcellino, 2022. "The financial accelerator mechanism: does frequency matter?," Working Papers 22-29, Federal Reserve Bank of Cleveland.
• Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," PIER Working Paper Archive 22-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.