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Report NEP-FOR-2020-09-14
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:
- Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019, "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Paper, Norges Bank, number 2019/2, Jan.
- Maximilian Böck & Martin Feldkircher & Florian Huber, 2020, "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R," Globalization Institute Working Papers, Federal Reserve Bank of Dallas, number 395, Aug, DOI: 10.24149/gwp395.
- Berta, P. & Lovaglio, P.G. & Paruolo, P. & Verzillo, S., 2020, "Real Time Forecasting of Covid-19 Intensive Care Units demand," Health, Econometrics and Data Group (HEDG) Working Papers, HEDG, c/o Department of Economics, University of York, number 20/16, Aug.
- Alexander Chudik & M. Hashem Pesaran & Mahrad Sharifvaghefi, 2020, "Variable Selection in High Dimensional Linear Regressions with Parameter Instability," Globalization Institute Working Papers, Federal Reserve Bank of Dallas, number 394, Aug, revised 05 Aug 2024, DOI: 10.24149/gwp394r3.
- Alexander Chudik & M. Hashem Pesaran & Mahrad Sharifvaghefi, 2020, "Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks," CESifo Working Paper Series, CESifo, number 8475.
- Hinterlang, Natascha, 2020, "Predicting monetary policy using artificial neural networks," Discussion Papers, Deutsche Bundesbank, number 44/2020.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020, "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers, arXiv.org, number 2008.12477, Aug.
- Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2019, "Forecast density combinations with dynamic learning for large data sets in economics and finance," Working Paper, Norges Bank, number 2019/7, Mar.
- Robert C. Smit & Francesco Ravazzolo & Luca Rossini, 2020, "Dynamic Bayesian forecasting of English Premier League match results with the Skellam distribution," BEMPS - Bozen Economics & Management Paper Series, Faculty of Economics and Management at the Free University of Bozen, number BEMPS72, Sep.