Report NEP-FOR-2020-07-27

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:

• E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020. "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers 2006.16383, arXiv.org, revised Aug 2020.
• Marijn A. Bolhuis & Brett Rayner, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 2020/045, International Monetary Fund.
• Mr. Serhan Cevik, 2020. "Where Should We Go? Internet Searches and Tourist Arrivals," IMF Working Papers 2020/022, International Monetary Fund.
• Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
• Ana Beatriz Galvão & Marta Lopresto, 2020. "Real-time Probabilistic Nowcasts of UK Quarterly GDP Growth using a Mixed-Frequency Bottom-up Approach," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-06, Economic Statistics Centre of Excellence (ESCoE).
• Bo Zhao, 2020. "Forecasting the New England States’ Tax Revenues in the Time of the COVID-19 Pandemic," Current Policy Perspectives 88356, Federal Reserve Bank of Boston.
• Massimo Guidolin & Manuela Pedio, 2020. "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," BAFFI CAREFIN Working Papers 20145, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
• Marijn A. Bolhuis & Brett Rayner, 2020. "The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data," IMF Working Papers 2020/044, International Monetary Fund.
• Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
• Zhou, W. & O’Neill, E. & Moncaster, A. & Reiner D. & Guthrie, P., 2020. "Forecasting Urban Residential Stock Turnover Dynamics using System Dynamics and Bayesian Model Averaging," Cambridge Working Papers in Economics 2054, Faculty of Economics, University of Cambridge.
• Elena Andreou & Eric Ghysels, 2020. "Predicting the VIX and the Volatility Risk Premium: The Role of Short-run Funding Spreads Volatility Factors," University of Cyprus Working Papers in Economics 04-2020, University of Cyprus Department of Economics.
• Sana Ben Hamida & Wafa Abdelmalek & Fathi Abid, 2020. "Applying Dynamic Training-Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility," Papers 2007.07207, arXiv.org.
• Frank Schorfheide & Dongho Song, 2020. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," Working Papers 20-26, Federal Reserve Bank of Philadelphia.
• Zachary Parolin & Megan Curran & Christoper Wimer, 2020. "The CARES ACT and Poverty in the COVID-19 Crisis: Promises and Pitfalls of the Recovery Rebates and Expanded Unemployment Benefits," Poverty and Social Policy Brief 2048, Center on Poverty and Social Policy, Columbia University.
• Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
• Reichlin, Lucrezia & Andreini, Paolo & Hasenzagl, Thomas & Senftleben-König, Charlotte & Strohsal, Till, 2020. "Nowcasting German GDP," CEPR Discussion Papers 14323, C.E.P.R. Discussion Papers.
• Zachary Parolin & Christoper Wimer, 2020. "Forecasting Estimates of Poverty During the COVID-19 Crisis," Poverty and Social Policy Brief 2046, Center on Poverty and Social Policy, Columbia University.
• Gorshkova, Taisiya (Горшкова, Таисия) & Turuntseva, Marina (Турунцева, Марина), 2020. "Theoretical approaches to forecasting regional macro-indicators [Теоретические Подходы К Прогнозированию Региональных Макропоказателей]," Working Papers 032042, Russian Presidential Academy of National Economy and Public Administration.