Forecast combinations in a DSGE-VAR lab

Mauro Costantini, Ulrich Gunter, Robert Kunst

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 method that fuses
test-based and Bates-Granger weighting. For a realistic simulation
design, we generate multivariate time-series samples from a macroe-
conomic DSGE-VAR 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. Uni-
form averages may be the weighting scheme that is most robust to
empirically observed irregularities.

Department of Economics
External organisation(s)
Brunel University London, MODUL University Vienna
No. of pages
Publication date
Austrian Fields of Science 2012
502025 Econometrics
ASJC Scopus subject areas
Economics, Econometrics and Finance(all)
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