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Report NEP-FOR-2021-03-15
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:
- Nadiia Shapovalenko, 2021, "A BVAR Model for Forecasting Ukrainian Inflation," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 05-2021, Mar.
- Nélida Díaz Sobrino & Corinna Ghirelli & Samuel Hurtado & Javier J. Pérez & Alberto Urtasun, 2020, "The narrative about the economy as a shadow forecast: an analysis using Banco de España quarterly reports," Working Papers, Banco de España, number 2042, Dec.
- Dautel, Alexander Jakob & Härdle, Wolfgang Karl & Lessmann, Stefan & Seow, Hsin-Vonn, 2020, "Forex exchange rate forecasting using deep recurrent neural networks," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2020-006.
- Xingcai Zhou & Jiangyan Wang, 2021, "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers, arXiv.org, number 2103.00711, Feb.
- Afees A. Salisu & Rangan Gupta & Qiang Ji, 2021, "Forecasting Oil Price over 150 Years: The Role of Tail Risks," Working Papers, University of Pretoria, Department of Economics, number 202120, Mar.
- Stamer, Vincent, 2021, "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers, Kiel Institute for the World Economy, number 2179.
- Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021, "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg, number 2021-01.