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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. Yong Yin issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, arXiv.org, number 2302.08002, Feb, revised Oct 2023.
- Sam Dannels, 2023, "Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data," Papers, arXiv.org, number 2302.10490, Feb.
- Serena Ng & Susannah Scanlan, 2023, "Constructing High Frequency Economic Indicators by Imputation," Papers, arXiv.org, number 2303.01863, Mar, 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, London School of Economics and Political Science, LSE Library, number 118366, Mar.
- Joseph de Vilmarest & Nicklas Werge, 2023, "An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition," Papers, arXiv.org, number 2303.01855, Mar, revised Jun 2024.
- Hilde C. Bjornland & Yoosoon Chang & Jamie L. Cross, 2024, "Oil and the Stock Market Revisited: A Mixed Functional VAR Approach," CAEPR Working Papers, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington, number 2023-005 Classification-1, Jul.
- 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, London School of Economics and Political Science, LSE Library, number 113647, Feb.