Forecast Combinations in a DSGE-VAR Lab

Costantini, Mauro, Gunter, Ulrich and Kunst, Robert M. ORCID: https://orcid.org/0000-0001-6831-2471 (2017) Forecast Combinations in a DSGE-VAR Lab. Journal of Forecasting, 36 (3), pp. 305-324.

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Abstract or Table of Contents

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. (Author's abstract)

Item Type: Article in Academic Journal
Keywords: forecasting; combining forecasts; encompassing tests; model selection; time series
Status: Published
Date Deposited: 10 May 2017 13:20
Last Modified: 09 Jan 2019 11:46
Identification Number or DOI: 10.1002/for.2427
ISSN: 0277-6693
URI: https://irihs.ihs.ac.at/id/eprint/4276

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