Report NEP-FOR-2020-03-23

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.

Other reports in NEP-FOR

The following items were announced in this report:

• Firmin Doko Tchatoka & Qazi Haque, 2020. "On bootstrapping tests of equal forecast accuracy for nested models," School of Economics and Public Policy Working Papers 2020-03, University of Adelaide, School of Economics and Public Policy.
• Marijn A. Bolhuis & Brett Rayner, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 20/45, International Monetary Fund.
• Rybacki, Jakub, 2020. "Polish GDP Forecast Errors: A Tale of Ineffectiveness," MPRA Paper 98952, University Library of Munich, Germany.
• Manav Kaushik & A K Giri, 2020. "Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques," Papers 2002.10247, arXiv.org.
• Michael D. Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2020. "Online Estimation of DSGE Models," NBER Working Papers 26826, National Bureau of Economic Research, Inc.
• Marijn A. Bolhuis & Brett Rayner, 2020. "The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data," IMF Working Papers 20/44, International Monetary Fund.
• Chengyuan Zhang & Fuxin Jiang & Shouyang Wang & Shaolong Sun, 2020. "A New Decomposition Ensemble Approach for Tourism Demand Forecasting: Evidence from Major Source Countries," Papers 2002.09201, arXiv.org.
• Yang Yifan & Guo Ju'e & Sun Shaolong & Li Yixin, 2020. "A new hybrid approach for crude oil price forecasting: Evidence from multi-scale data," Papers 2002.09656, arXiv.org.