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Report NEP-FOR-2020-03-02
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:
- Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020, "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/20/02, Feb.
- Hyeongwoo Kim & Soohyon Kim, 2020, "Common Factor Augmented Forecasting Models for the US Dollar-Korean Won Exchange Rate," Working Papers, Economic Research Institute, Bank of Korea, number 2020-5, Feb.
- Yun Bai & Xixi Li & Hao Yu & Suling Jia, 2020, "Crude oil price forecasting incorporating news text," Papers, arXiv.org, number 2002.02010, Jan, revised Jul 2021.
- Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2020, "Beating the naive: Combining LASSO with naive intraday electricity price forecasts," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/20/01, Feb.
- McAdam, Peter & Warne, Anders, 2020, "Density forecast combinations: the real-time dimension," Working Paper Series, European Central Bank, number 2378, Feb.
- Dominique Guegan & Matteo Iacopini, 2018, "Nonparametric forecasting of multivariate probability density functions," Post-Print, HAL, number halshs-01821815, Mar, DOI: 10.48550/arXiv.1803.06823.
- Van Nguyen, Phuong, 2020, "Evaluating the forecasting accuracy of the closed- and open economy New Keynesian DSGE models," Dynare Working Papers, CEPREMAP, number 59, Feb.
- Lior Sidi, 2020, "Improving S&P stock prediction with time series stock similarity," Papers, arXiv.org, number 2002.05784, Feb.
- Grammig, Joachim & Hanenberg, Constantin & Schlag, Christian & Sönksen, Jantje, 2020, "Diverging roads: Theory-based vs. machine learning-implied stock risk premia," University of Tübingen Working Papers in Business and Economics, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics, number 130, DOI: 10.15496/publikation-39286.