Forecast Combinations in a DSGE-VAR Lab
- Author(s)
- Mauro Costantini, Ulrich Gunter, Robert Kunst
- Abstract
We explore the benefits of forecast combinations based on forecast-encompassing tests compared to simple averages and to Bates–Granger combinations. We also consider a new combination algorithm that fuses test-based and Bates–Granger weighting. For a realistic simulation design, we generate multivariate time series samples from a macroeconomic DSGE-VAR (dynamic stochastic general equilibrium–vector autoregressive) model. Results generally support Bates–Granger over uniform weighting, whereas benefits of test-based weights depend on the sample size and on the prediction horizon. In a corresponding application to real-world data, simple averaging performs best. Uniform averages may be the weighting scheme that is most robust to empirically observed irregularities.
- Organisation(s)
- Department of Economics
- External organisation(s)
- Brunel University London, MODUL University Vienna, IHS - Institut für Höhere Studien und wissenschaftliche Forschung
- Journal
- Journal of Forecasting
- Volume
- 36
- Pages
- 305-324
- No. of pages
- 20
- ISSN
- 0277-6693
- DOI
- https://doi.org/10.1002/for.2427
- Publication date
- 05-2016
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 502025 Econometrics, 101018 Statistics, 502018 Macroeconomics
- Keywords
- ASJC Scopus subject areas
- Computer Science Applications, Statistics, Probability and Uncertainty, Modelling and Simulation, Strategy and Management, Management Science and Operations Research
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/f976f772-a5bf-4448-952f-d878644f8dfb