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Report NEP-FOR-2021-01-11
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:
- Olivier Dessaint & Thierry Foucault & Laurent Frésard, 2020, "Does Big Data Improve Financial Forecasting? The Horizon Effect," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-106, Nov.
- Xin Sheng & Rangan Gupta & Afees A. Salisu & Elie Bouri, 2021, "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Working Papers, University of Pretoria, Department of Economics, number 202101, Jan.
- Mehmet A. Soytas & Hasan M. Ertugrul & Talat Ulussever, 2020, "Nonlinear Excess Demand Model for Electricity Price Prediction," Working Papers, Economic Research Forum, number 1449, Dec, revised 20 Dec 2020.
- Bauwens, Luc & Otranto, Edoardo, 2020, "Modelling Realized Covariance Matrices: a Class of Hadamard Exponential Models," LIDAM Discussion Papers CORE, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE), number 2020034, Nov.
- Akyildirim, Erdinc & Cepni, Oguzhan & Corbet, Shaen & Uddin, Gazi Salah, 2020, "Forecasting Mid-price Movement of Bitcoin Futures Using Machine Learning," Working Papers, Copenhagen Business School, Department of Economics, number 20-2020, Dec.
- Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna & Mark E. Wohar, 2021, "Uncertainty and Predictability of Real Housing Returns in the United Kingdom: A Regional Analysis," Working Papers, University of Pretoria, Department of Economics, number 202102, Jan.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020, "Machine Learning Advances for Time Series Forecasting," Papers, arXiv.org, number 2012.12802, Dec, revised Apr 2021.