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Bayesian aggregation of two forecasts in the partial information framework

Author

Listed:
  • Ernst, Philip
  • Pemantle, Robin
  • Satopää, Ville
  • Ungar, Lyle

Abstract

We generalize the results of Satopää et al (in press, 2015) by showing how the Gaussian aggregator may be computed in a setting where parameter estimation is not required. We proceed to provide an explicit formula for a “one-shot” aggregation problem with two forecasters.

Suggested Citation

  • Ernst, Philip & Pemantle, Robin & Satopää, Ville & Ungar, Lyle, 2016. "Bayesian aggregation of two forecasts in the partial information framework," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 170-180.
    • Handle: RePEc:eee:stapro:v:119:y:2016:i:c:p:170-180
    DOI: 10.1016/j.spl.2016.07.018
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    References listed on IDEAS

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    1. Hong, Lu & Page, Scott, 2009. "Interpreted and generated signals," Journal of Economic Theory, Elsevier, vol. 144(5), pages 2174-2196, September.
    2. Christian Meyer, 2009. "The Bivariate Normal Copula," Papers 0912.2816, arXiv.org.
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    Cited by:

    1. Yakov Babichenko & Dan Garber, 2021. "Learning Optimal Forecast Aggregation in Partial Evidence Environments," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 628-641, May.
    2. Itai Areili & Yakov Babichenko & Rann Smorodinsky, 2017. "Robust Forecast Aggregation," Papers 1710.02838, arXiv.org, revised Feb 2018.

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