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://ucris.univie.ac.at/portal/en/publications/forecast-combinations-in-a-dsgevar-lab(f976f772-a5bf-4448-952f-d878644f8dfb).html