Forecast Combinations in a DSGE‐VAR Lab |
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Authors: | Mauro Costantini Ulrich Gunter Robert M Kunst |
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Institution: | 1. Department of Economics and Finance, Brunel University London, Uxbridge, UK;2. Department of Tourism and Service Management, MODUL University Vienna, Austria;3. Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria;4. Department of Economics, University of Vienna, Austria |
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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. Copyright © 2016 John Wiley & Sons, Ltd. |
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Keywords: | forecasting combining forecasts encompassing tests model selection time series |
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