Forecast combination under heavy-tailed errors

Gang Cheng, Sicong Wang, Yuhong Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has been done to obtain reliable forecast combinations for such situations. The familiar forecast combination methods, such as simple average, least squares regression or those based on the variance-covariance of the forecasts, may perform very poorly due to the fact that outliers tend to occur, and they make these methods have unstable weights, leading to un-robust forecasts. To address this problem, in this paper, we propose two nonparametric forecast combination methods. One is specially proposed for the situations in which the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student’s t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to a shortage of data and/or an evolving data-generating process. Adaptive risk bounds of both methods are developed. They show that the resulting combined forecasts yield near optimal mean forecast errors relative to the candidate forecasts. Simulations and a real example demonstrate their superior performance in that they indeed tend to have significantly smaller prediction errors than the previous combination methods in the presence of forecast outliers.

Original languageEnglish (US)
Pages (from-to)797-824
Number of pages28
Issue number4
StatePublished - Dec 2015

Bibliographical note

Publisher Copyright:
© 2015 by the authors; licensee MDPI, Basel, Switzerland.


  • Forecast combination
  • Heavy tails
  • Nonparametric forecast combination
  • Robustness
  • Time series models


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