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Report NEP-FOR-2019-09-02
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:
- Knüppel, Malte & Krüger, Fabian, 2019, "Forecast uncertainty, disagreement, and the linear pool," Discussion Papers, Deutsche Bundesbank, number 28/2019.
- Engelke, Carola & Heinisch, Katja & Schult, Christoph, 2019, "How forecast accuracy depends on conditioning assumptions," IWH Discussion Papers, Halle Institute for Economic Research (IWH), number 18/2019.
- Manel Hamdi & Walid Chkili, 2019, "An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?," Working Papers, Economic Research Forum, number 13, Aug, revised 21 Aug 2019.
- Lisa-Cheree Martin, 2019, "Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP," Working Papers, Stellenbosch University, Department of Economics, number 12/2019.
- António Rua & Hossein Hassani, 2019, "Monthly Forecasting of GDP with Mixed Frequency Multivariate Singular Spectrum Analysis," Working Papers, Banco de Portugal, Economics and Research Department, number w201913.
- N. Meade & J. E. Beasley & C. J. Adcock, 2019, "Quantitative portfolio selection: using density forecasting to find consistent portfolios," Papers, arXiv.org, number 1908.08442, Aug, revised Jun 2020.
- Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019, "Conditional variance forecasts for long-term stock returns," Graz Economics Papers, University of Graz, Department of Economics, number 2019-08, Aug.
- Janzen, Joseph & Legrand, Nicolas, , "Wheat Futures Trading Volume Forecasting and the Value of Extended Trading Hours," 2019 Annual Meeting, July 21-23, Atlanta, Georgia, Agricultural and Applied Economics Association, number 290798, DOI: 10.22004/ag.econ.290798.
- David Byrd & Tucker Hybinette Balch, 2019, "Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency," Papers, arXiv.org, number 1908.08168, Aug.
- Mina Lee & Joseph Y. J. Chow & Gyugeun Yoon & Brian Yueshuai He, 2019, "Forecasting e-scooter substitution of direct and access trips by mode and distance," Papers, arXiv.org, number 1908.08127, Aug, revised Apr 2021.