Report NEP-ETS-2023-04-03

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:

• Chen Liu & Chao Wang & Minh-Ngoc Tran & Robert Kohn, 2023. "Deep Learning Enhanced Realized GARCH," Papers 2302.08002, arXiv.org, revised Oct 2023.
• Sam Dannels, 2023. "Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data," Papers 2302.10490, arXiv.org.
• Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
• Cai, Hanqing & Wang, Tengyao, 2023. "Estimation of high-dimensional change-points under a group sparsity structure," LSE Research Online Documents on Economics 118366, London School of Economics and Political Science, LSE Library.
• Joseph de Vilmarest & Nicklas Werge, 2023. "An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition," Papers 2303.01855, arXiv.org, revised Jun 2024.
• Hilde C. Bjornland & Yoosoon Chang & Jamie L. Cross, 2023. "Oil and the Stock Market Revisited: A Mixed Functional VAR Approach," CAEPR Working Papers 2023-005 Classification-1, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
• Riani, Marco & Atkinson, Anthony C. & Corbellini, Aldo & Farcomeni, Alessio & Laurini, Fabrizio, 2022. "Information criteria for outlier detection avoiding arbitrary significance levels," LSE Research Online Documents on Economics 113647, London School of Economics and Political Science, LSE Library.