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Forecast Combinations in a DSGE‐VAR Lab

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  • Mauro Costantini
  • Ulrich Gunter
  • Robert M. Kunst

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 macroeconomic 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. Uniform averages may be the weighting scheme that is most robust to empirically observed irregularities.
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Suggested Citation

  • Mauro Costantini & Ulrich Gunter & Robert M. Kunst, 2017. "Forecast Combinations in a DSGE‐VAR Lab," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(3), pages 305-324, April.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:3:p:305-324
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    Cited by:

    1. Bingzi Jin & Xiaojie Xu, 2025. "Predicting open interest in thermal coal futures using machine learning," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(4), pages 795-809, December.
    2. Bingzi Jin & Xiaojie Xu, 2025. "Predictions of residential property price indices for China via machine learning models," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1481-1513, April.
    3. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    4. Bingzi Jin & Xiaojie Xu, 2025. "Machine learning price index forecasts of flat steel products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 97-117, March.
    5. Bingzi Jin & Xiaojie Xu, 2026. "Machine learning wholesale white wheat price index forecasts," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(1), pages 277-305, February.
    6. Bingzi Jin & Xiaojie Xu, 2025. "Steel price index forecasts through machine learning for northwest China," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(4), pages 811-833, December.
    7. Timo Dimitriadis & iaochun Liu & Julie Schnaitmann, 2023. "Encompassing Tests for Value at Risk and Expected Shortfall Multistep Forecasts Based on Inference on the Boundary," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 412-444.
    8. Ulrich Gunter & Irem Önder & Egon Smeral, 2020. "Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?," Forecasting, MDPI, vol. 2(3), pages 1-19, June.

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