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Forecast combinations in a DSGE-VAR lab

Author

Listed:
  • Costantini, Mauro

    (Department of Economics and Finance, Brunel University)

  • Gunter, Ulrich

    (Department of Tourism and Service Management, MODUL University Vienna)

  • Kunst, Robert M.

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna and Department of Economics, University of Vienna)

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.

Suggested Citation

  • Costantini, Mauro & Gunter, Ulrich & Kunst, Robert M., 2014. "Forecast combinations in a DSGE-VAR lab," Economics Series 309, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:309
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    File URL: https://irihs.ihs.ac.at/id/eprint/2911
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    Cited by:

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    2. 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.
    3. 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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    Keywords

    Combining forecasts; encompassing tests; model selection; time series; DSGE-VAR model;
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