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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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, arXiv.org, number 2006.16383, Jun, revised Aug 2020.
- Marijn A. Bolhuis & Brett Rayner, 2020, "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers, International Monetary Fund, number 2020/045, Feb.
- Mr. Serhan Cevik, 2020, "Where Should We Go? Internet Searches and Tourist Arrivals," IMF Working Papers, International Monetary Fund, number 2020/022, Jan.
- 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, C.E.P.R. Discussion Papers, number 14267, Jan.
- 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, Economic Statistics Centre of Excellence (ESCoE), number ESCoE DP-2020-06, May.
- Bo Zhao, 2020, "Forecasting the New England States’ Tax Revenues in the Time of the COVID-19 Pandemic," Current Policy Perspectives, Federal Reserve Bank of Boston, number 88356, Jul.
- Massimo Guidolin & Manuela Pedio, 2020, "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," BAFFI CAREFIN Working Papers, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy, number 20145.
- Marijn A. Bolhuis & Brett Rayner, 2020, "The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data," IMF Working Papers, International Monetary Fund, number 2020/044, Feb.
- Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020, "Large Time-Varying Volatility Models for Electricity Prices," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 05/2020, Jul.
- 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, Faculty of Economics, University of Cambridge, number 2054, Jun.
- 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, University of Cyprus Department of Economics, number 04-2020, Mar.
- Sana Ben Hamida & Wafa Abdelmalek & Fathi Abid, 2020, "Applying Dynamic Training-Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility," Papers, arXiv.org, number 2007.07207, Jun.
- Frank Schorfheide & Dongho Song, 2020, "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," Working Papers, Federal Reserve Bank of Philadelphia, number 20-26, Jul, DOI: 10.21799/frbp.wp.2020.26.
- 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, Center on Poverty and Social Policy, Columbia University, number 2048, Jun.
- Florian Huber & Luca Rossini, 2020, "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers, arXiv.org, number 2006.16333, Jun, revised Mar 2021.
- Reichlin, Lucrezia & Andreini, Paolo & Hasenzagl, Thomas & Senftleben-König, Charlotte & Strohsal, Till, 2020, "Nowcasting German GDP," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14323, Jan.
- Zachary Parolin & Christoper Wimer, 2020, "Forecasting Estimates of Poverty During the COVID-19 Crisis," Poverty and Social Policy Brief, Center on Poverty and Social Policy, Columbia University, number 2046, Apr.
- Gorshkova, Taisiya (Горшкова, Таисия) & Turuntseva, Marina (Турунцева, Марина), 2020, "Theoretical approaches to forecasting regional macro-indicators
[Теоретические Подходы К Прогнозированию Региональных Макропоказателей]," Working Papers, Russian Presidential Academy of National Economy and Public Administration, number 032042, Mar.