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Report NEP-FOR-2019-10-28
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.
Other reports in NEP-FOR
The following items were announced in this report:
- Florian Eckert & Rob J Hyndman & Anastasios Panagiotelis, 2019. "Forecasting Swiss Exports using Bayesian Forecast Reconciliation," KOF Working papers 19-457, KOF Swiss Economic Institute, ETH Zurich.
- Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
- Anastasios Panagiotelis & Puwasala Gamakumara & George Athanasopoulos & Rob J Hyndman, 2019. "Forecast Reconciliation: A geometric View with New Insights on Bias Correction," Monash Econometrics and Business Statistics Working Papers 18/19, Monash University, Department of Econometrics and Business Statistics.
- Claudio Borio & Mathias Drehmann & Dora Xia Author-X-Name_First: Dora, 2019. "Predicting recessions: financial cycle versus term spread," BIS Working Papers 818, Bank for International Settlements.
- Patrick Leung & Catherine S. Forbes & Gael M Martin & Brendan McCabe, 2019. "Forecasting Observables with Particle Filters: Any Filter Will Do!," Monash Econometrics and Business Statistics Working Papers 22/19, Monash University, Department of Econometrics and Business Statistics.
- Gaglianone, Wagner Piazza & Issler, João Victor, 2019. "Microfounded forecasting," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 813, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Niko Hauzenberger & Florian Huber & Gary Koop & Luca Onorante, 2019. "Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models," Papers 1910.10779, arXiv.org, revised Sep 2021.
- Nikolas Kuschnig & Lukas Vashold, 2019. "BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R," Department of Economics Working Papers wuwp296, Vienna University of Economics and Business, Department of Economics.
- Uwe Hassler & Marc-Oliver Pohle, 2019. "Forecasting under Long Memory and Nonstationarity," Papers 1910.08202, arXiv.org.