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Report NEP-FOR-2021-04-05
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:
- Arkadiusz Jedrzejewski & Grzegorz Marcjasz & Rafal Weron, 2021. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO," WORking papers in Management Science (WORMS) WORMS/21/04, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
- Afees A. Salisu & Rangan Gupta & Riza Demirer, 2021. "Global Financial Cycle and the Predictability of Oil Market Volatility: Evidence from a GARCH-MIDAS Model," Working Papers 202121, University of Pretoria, Department of Economics.
- Rizzi, Silvia & Vaupel, James W, 2021. "Forecasting Imminent Deaths," OSF Preprints ymw4t, Center for Open Science.
- Hannes Mueller & Christopher Rauh, 2021. "The Hard Problem of Prediction for Conflict Prevention," Working Papers 1244, Barcelona School of Economics.
- Arthur Thomas & Olivier Massol & Benoît Sévi, 2020. "How are Day-Ahead Prices Informative for Predicting the Next Day’s Consumption of Natural Gas ?," Working Papers hal-03178474, HAL.
- J. Daniel Aromí & Martín Llada, 2020. "Forecasting inflation with twitter," Asociación Argentina de Economía Política: Working Papers 4308, Asociación Argentina de Economía Política.
- Huiwen Wang & Wenyang Huang & Shanshan Wang, 2021. "Forecasting open-high-low-close data contained in candlestick chart," Papers 2104.00581, arXiv.org.
- Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
- Zhaoxing Gao & Ruey S. Tsay, 2021. "Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data," Papers 2103.14626, arXiv.org.