Report NEP-FOR-2020-04-06

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:

• Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
• Robert Lehmann & Magnus Reif, 2020. "Tracking and Predicting the German Economy: ifo vs. PMI," CESifo Working Paper Series 8145, CESifo.
• Barbara Rossi, 2019. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," Working Papers 1162, Barcelona School of Economics.
• Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
• Felix Haase & Matthias Neuenkirch, 2020. "Predictability of Bull and Bear Markets: A New Look at Forecasting Stock Market Regimes (and Returns) in the US," Research Papers in Economics 2020-01, University of Trier, Department of Economics.
• Yuan, Huiling & Zhou, Yong & Zhang, Zhiyuan & Cui, Xiangyu, 2020. "Forecasting security's volatility using low-frequency historical data, high-frequency historical data and option-implied volatility," SocArXiv vdsqf, Center for Open Science.
• Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2020. "Forecasting natural gas prices using highly flexible time-varying parameter models," Working Papers 2020-01, University of Tasmania, Tasmanian School of Business and Economics.
• Yuan, Huiling & Zhou, Yong & Xu, Lu & Sun, Yulei & Cui, Xiangyu, 2020. "A New Volatility Model: GQARCH-Ito Model," SocArXiv hkzdr, Center for Open Science.
• Alexandros Botsis & Christoph Görtz & Plutarchos Sakellaris, 2020. "Quantifying Qualitative Survey Data: New Insights on the (Ir)Rationality of Firms' Forecasts," CESifo Working Paper Series 8148, CESifo.