Report NEP-FOR-2020-06-22

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:

• Daiki Maki & Yasushi Ota, 2020. "The impacts of asymmetry on modeling and forecasting realized volatility in Japanese stock markets," Papers 2006.00158, arXiv.org.
• Zidong An & João Tovar Jalles, 2020. "On the Performance of US Fiscal Forecasts: Government vs. Private Information," Working Papers REM 2020/0130, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
• Sander Barendse & Andrew J. Patton, 2020. "Comparing Predictive Accuracy in the Presence of a Loss Function Shape Parameter," Economics Series Working Papers 909, University of Oxford, Department of Economics.
• Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2020. "The Role of Investor Sentiment in Forecasting Housing Returns in China: A Machine Learning Approach," Working Papers 202055, University of Pretoria, Department of Economics.
• Afees A. Salisu & Rangan Gupta & Riza Demirer, 2020. "A Note on Uncertainty due to Infectious Diseases and Output Growth of the United States: A Mixed-Frequency Forecasting Experiment," Working Papers 202050, University of Pretoria, Department of Economics.
• Xie, Tian & Yu, Jun & Zeng, Tao, 2020. "Econometric Methods and Data Science Techniques: A Review of Two Strands of Literature and an Introduction to Hybrid Methods," Economics and Statistics Working Papers 16-2020, Singapore Management University, School of Economics.
• Mahmut Gunay, 2020. "Nowcasting Turkish GDP Growth with Targeted Predictors: Fill in the Blanks," Working Papers 2006, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
• Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers 202051, University of Pretoria, Department of Economics.
• Shen Gao & Chenghan Hou & Bao H. Nguyen, 2020. "Forecasting natural gas prices using highly flexible time-varying parameter models," CAMA Working Papers 2020-30, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
• Lars Elend & Sebastian A. Tideman & Kerstin Lopatta & Oliver Kramer, 2020. "Earnings Prediction with Deep Learning," Papers 2006.03132, arXiv.org, revised Oct 2020.