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.

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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 28/2019, Deutsche Bundesbank.
• Engelke, Carola & Heinisch, Katja & Schult, Christoph, 2019. "How forecast accuracy depends on conditioning assumptions," IWH Discussion Papers 18/2019, Halle Institute for Economic Research (IWH).
• 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 13, Economic Research Forum, revised 21 Aug 2019.
• Lisa-Cheree Martin, 2019. "Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP," Working Papers 12/2019, Stellenbosch University, Department of Economics.
• António Rua & Hossein Hassani, 2019. "Monthly Forecasting of GDP with Mixed Frequency Multivariate Singular Spectrum Analysis," Working Papers w201913, Banco de Portugal, Economics and Research Department.
• N. Meade & J. E. Beasley & C. J. Adcock, 2019. "Quantitative portfolio selection: using density forecasting to find consistent portfolios," Papers 1908.08442, arXiv.org, revised Jun 2020.
• Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019. "Conditional variance forecasts for long-term stock returns," Graz Economics Papers 2019-08, University of Graz, Department of Economics.
• Janzen, Joseph & Legrand, Nicolas, 2019. "Wheat Futures Trading Volume Forecasting and the Value of Extended Trading Hours," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 290798, Agricultural and Applied Economics Association.
• David Byrd & Tucker Hybinette Balch, 2019. "Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency," Papers 1908.08168, arXiv.org.
• 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 1908.08127, arXiv.org, revised Apr 2021.