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Report NEP-FOR-2024-01-01
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:
- Amor Aniss Benmoussa, Reinhard Ellwanger, Stephen Snudden, 2023, "Carpe Diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?," LCERPA Working Papers, Laurier Centre for Economic Research and Policy Analysis, number bm0141, Dec.
- Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023, "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers, Laurier Centre for Economic Research and Policy Analysis, number bm0142, Dec.
- Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023, "Predictive Density Combination Using a Tree-Based Synthesis Function," Working Papers, Federal Reserve Bank of Cleveland, number 23-30, Nov, DOI: 10.26509/frbc-wp-202330.
- Chaya Weerasinghe & Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier, 2023, "ABC-based Forecasting in State Space Models," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 12/23.
- Parth Daxesh Modi & Kamyar Arshi & Pertami J. Kunz & Abdelhak M. Zoubir, 2023, "A Data-driven Deep Learning Approach for Bitcoin Price Forecasting," Papers, arXiv.org, number 2311.06280, Oct.