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Econometrics 2015, 3(4), 797-824; doi:10.3390/econometrics3040797

Forecast Combination under Heavy-Tailed Errors

School of Statistics, University of Minnesota at Twin Cities, 313 Ford Hall, 224 Church Street SE, Minneapolis, MN 55455, USA
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Academic Editor: Isabel Casas
Received: 22 August 2015 / Revised: 9 November 2015 / Accepted: 10 November 2015 / Published: 23 November 2015
(This article belongs to the Special Issue Nonparametric Methods in Econometrics)
View Full-Text   |   Download PDF [371 KB, uploaded 23 November 2015]

Abstract

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. View Full-Text
Keywords: forecast combination; heavy tails; robustness; time series models; nonparametric forecast combination forecast combination; heavy tails; robustness; time series models; nonparametric forecast combination
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cheng, G.; Wang, S.; Yang, Y. Forecast Combination under Heavy-Tailed Errors. Econometrics 2015, 3, 797-824.

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