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A note on the estimation of optimal weights for density forecast combinations

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  • Pauwels, Laurent L.
  • Vasnev, Andrey L.

Abstract

The problem of finding appropriate weights for combining several density forecasts is an important issue that is currently being debated in the forecast combination literature. A recent paper by Hall and Mitchell (2007) proposes that density forecasts be combined using the weights obtained from solving an optimization problem. This paper documents the properties of this optimization problem through a series of simulation experiments. When the number of forecasting periods is relatively small, the optimization problem often produces solutions that are dominated by a number of simple alternatives.

Suggested Citation

  • Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
  • Handle: RePEc:eee:intfor:v:32:y:2016:i:2:p:391-397
    DOI: 10.1016/j.ijforecast.2015.09.002
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    References listed on IDEAS

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    Cited by:

    1. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    2. Edward Knotek & Saeed Zaman, 2020. "Real-time density nowcasts of US inflation: a model-combination approach," Working Papers 2015, University of Strathclyde Business School, Department of Economics.
    3. Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2019. "Higher Moment Constraints for Predictive Density Combinations," Working Papers BAWP-2019-01, University of Sydney Business School, Discipline of Business Analytics.
    4. Li, Jingrui & Wang, Rui & Wang, Jianzhou & Li, Yifan, 2018. "Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms," Energy, Elsevier, vol. 144(C), pages 243-264.
    5. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España;Working Papers Homepage.
    6. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    7. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    8. Pauwels, Laurent, 2019. "Predicting China’s Monetary Policy with Forecast Combinations," Working Papers BAWP-2019-07, University of Sydney Business School, Discipline of Business Analytics.
    9. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    10. McAdam, Peter & Warne, Anders, 2020. "Density forecast combinations: the real-time dimension," Working Paper Series 2378, European Central Bank.

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