Report NEP-FOR-2020-06-15

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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The following items were announced in this report:

• Item repec:wrk:wrkemf:33 is not listed on IDEAS anymore

• Qiu, Yue & Xie, Tian & Yu, Jun, 2020. "Forecast combinations in machine learning," Economics and Statistics Working Papers 13-2020, Singapore Management University, School of Economics.
• Roberto Baviera & Giuseppe Messuti, 2020. "Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England," Papers 2005.13005, arXiv.org, revised Oct 2020.
• Tim Janke & Florian Steinke, 2020. "Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing," Papers 2005.13417, arXiv.org.
• Laurent Pauwels & Peter Radchenko & Andrey L. Vasnev, 2020. "High Moment Constraints for Predictive Density Combination," CAMA Working Papers 2020-45, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, revised Jun 2023.
• Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
• Marek Kwas & Alessia Paccagnini & Michal Rubaszek, 2020. "Common factors and the dynamics of cereal prices. A forecasting perspective," CAMA Working Papers 2020-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
• Michael Puglia & Adam Tucker, 2020. "Machine Learning, the Treasury Yield Curve and Recession Forecasting," Finance and Economics Discussion Series 2020-038, Board of Governors of the Federal Reserve System (U.S.).
• Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "The Role of Global Economic Conditions in Forecasting Gold Market Volatility: Evidence from a GARCH-MIDAS Approach," Working Papers 202043, University of Pretoria, Department of Economics.
• Nino Antulov-Fantulin & Tian Guo & Fabrizio Lillo, 2020. "Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets," Papers 2005.09356, arXiv.org, revised Dec 2020.
• Kalamara, Eleni & Turrell, Arthur & Redl, Chris & Kapetanios, George & Kapadia, Sujit, 2020. "Making text count: economic forecasting using newspaper text," Bank of England working papers 865, Bank of England.
• Rui Dong & Raymond Fisman & Yongxiang Wang & Nianhang Xu, 2019. "Air Pollution, Affect, and Forecasting Bias: Evidence from Chinese Financial Analysts," Boston University - Department of Economics - The Institute for Economic Development Working Papers Series dp-345, Boston University - Department of Economics.
• Wang, Dandan, 2020. "Forecasting gasoline prices with mixed random forest error correction models," UC3M Working papers. Economics 30557, Universidad Carlos III de Madrid. Departamento de Economía.
• Xinyue Cui & Zhaoyu Xu & Yue Zhou, 2020. "Using Machine Learning to Forecast Future Earnings," Papers 2005.13995, arXiv.org.
• Camille Cornand & Paul Hubert, 2020. "On the external validity of experimental inflation forecasts: A comparison with five categories of field expectations," SciencePo Working papers Main halshs-01890770, HAL.
• Item repec:wrk:wrkemf:32 is not listed on IDEAS anymore

• Tobias Hartl, 2020. "Macroeconomic Forecasting with Fractional Factor Models," Papers 2005.04897, arXiv.org.
• Dimitris Korobilis, 2019. "High-dimensional macroeconomic forecasting using message passing algorithms," Working Papers 2019-07, Business School - Economics, University of Glasgow.
• Neeti Mathur & Himanshu Mathur, 2020. "Application of GARCH Models For Volatility Modelling of Stock Market Returns: Evidences From BSE India," Proceedings of Business and Management Conferences 10112533, International Institute of Social and Economic Sciences.
• Kristof Decock & Michela Bergamini & Koenraad Debackere & Enrico Lupi & Anne Mieke Vandamme & Bart Van Looy, 2020. "Predicting when peaks will occur, ex ante. Insights from the COVID-19 Pandemic in Italy and Belgium," Working Papers of Department of Management, Strategy and Innovation, Leuven 654760, KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven.