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Report NEP-FOR-2020-08-31
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:
- Shafiullah Qureshi & Ba M. Chu & Fanny S. Demers, 2020, "Forecasting Canadian GDP growth using XGBoost," Carleton Economic Papers, Carleton University, Department of Economics, number 20-14, Aug, revised 24 Aug 2020.
- Glocker, Christian & Kaniovski, Serguei, 2020, "Structural modeling and forecasting using a cluster of dynamic factor models," MPRA Paper, University Library of Munich, Germany, number 101874, Jul.
- Ryan Cumings-Menon & Minchul Shin, 2020, "Probability Forecast Combination via Entropy Regularized Wasserstein Distance," Working Papers, Federal Reserve Bank of Philadelphia, number 20-31/R, Aug, DOI: 10.21799/frbp.wp.2020.31.
- Fantazzini, Dean, 2020, "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," MPRA Paper, University Library of Munich, Germany, number 102315, Aug.
- L. Vanessa Smith & Nori Tarui & Takashi Yamagata, 2020, "Global fossil fuel consumption and carbon pricing: Forecasting and counterfactual analysis under alternative GDP scenarios," RIEEM Discussion Paper Series, Research Institute for Environmental Economics and Management, Waseda University, number 2004, Aug.
- Peng-Fei Dai & Xiong Xiong & Wei-Xing Zhou, 2020, "The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model," Papers, arXiv.org, number 2007.12838, Jul.
- Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020, "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers, arXiv.org, number 2007.13566, Jul, revised Dec 2022.
- Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2020, "Nowcasting with large Bayesian vector autoregressions," Working Paper Series, European Central Bank, number 2453, Aug.