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 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.
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.
| Original language | English |
|---|---|
| Number of pages | 57 |
| Publication status | Published - Dec 2014 |
Publication series
| Series | IHS economics series : working paper |
|---|---|
| Volume | 309 |
Austrian Fields of Science 2012
- 502025 Econometrics
Keywords
- combining forecasts, encompassing tests, model selection, time series, DSGE-VAR model