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.

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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 05-2021, Economics Section, The Graduate Institute of International Studies.
• 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 2042, Banco de España.
• 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 2020-006, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Xingcai Zhou & Jiangyan Wang, 2021. "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers 2103.00711, arXiv.org.
• Afees A. Salisu & Rangan Gupta & Qiang Ji, 2021. "Forecasting Oil Price over 150 Years: The Role of Tail Risks," Working Papers 202120, University of Pretoria, Department of Economics.
• Stamer, Vincent, 2021. "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers 2179, Kiel Institute for the World Economy (IfW Kiel).
• Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA 2021-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.