Forecast combinations in a DSGE-VAR lab

Mauro Costantini, Ulrich Gunter, and Robert M. 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.

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Year of publication:	2014
Authors:	Costantini, Mauro ; Gunter, Ulrich ; Kunst, Robert M.
Publisher:	Wien : IHS
Subject:	Combining forecasts \| encompassing tests \| model selection \| time series \| DSGE-VAR model \| Prognoseverfahren \| Forecasting model \| Zeitreihenanalyse \| Time series analysis \| Theorie \| Theory \| Modellierung \| Scientific modelling \| VAR-Modell \| VAR model