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Report NEP-FOR-2022-08-15
This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Rob J Hyndman issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-FOR
The following items were announced in this report:
- Francis X. Diebold & Maximilian Goebel & 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," Papers 2206.10721, arXiv.org, revised Jun 2023.
- Sylvia Kaufmann, 2022. "Covid-19 outbreak and beyond: A retrospect on the information content of registered short-time workers for GDP now- and forecasting," Working Papers 22.02R, Swiss National Bank, Study Center Gerzensee.
- Jo~ao B. Assunc{c}~ao & Pedro Afonso Fernandes, 2022. "Nowcasting the Portuguese GDP with Monthly Data," Papers 2206.06823, arXiv.org.
- Mueller, H. & Rauh, C. & Ruggieri, A., 2022. "Dynamic Early Warning and Action Model," Janeway Institute Working Papers 2213, Faculty of Economics, University of Cambridge.
- Jyldyz Djumalieva & Stef Garasto & Cath Sleeman, 2020. "Evaluating a new earnings indicator. Can we improve the timeliness of existing statistics on earnings by using salary information from online job adverts?," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-19, Economic Statistics Centre of Excellence (ESCoE).
- Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling," Papers 2206.14275, arXiv.org, revised Feb 2024.
- Jeonggil Song, 2022. "Predicting Economic Welfare with Images on Wealth," Papers 2206.14810, arXiv.org.
- Simone Tonini & Francesca Chiaromonte & Alessandro Giovannelli, 2022. "On the impact of serial dependence on penalized regression methods," LEM Papers Series 2022/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- S. Roderick Zhang & Bilal Farooq, 2022. "Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level," Papers 2206.09073, arXiv.org.