Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12652
Title: Forecast combinations in a DSGE-VAR lab
Authors: Costantini, M
Gunter, U
Kunst, R
Keywords: Forecasting;Combining forecasts;Encompassing tests;Model selection;Time series
Issue Date: 2016
Publisher: Wiley
Citation: Journal of Forecasting, (2016)
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.
URI: http://onlinelibrary.wiley.com/doi/10.1002/for.2427/abstract
http://bura.brunel.ac.uk/handle/2438/12652
DOI: http://dx.doi.org/10.1002/for.2427
ISSN: 1099-131X
Appears in Collections:Dept of Economics and Finance Research Papers

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