Econometrics | |
Forecast Combination under Heavy-Tailed Errors | |
Gang Cheng1  Sicong Wang2  Yuhong Yang2  | |
[1] School of Statistics, University of Minnesota at Twin Cities, 313 Ford Hall, 224 Church Street SE, Minneapolis, MN 55455, USA; | |
关键词: forecast combination; heavy tails; robustness; time series models; nonparametric forecast combination; | |
DOI : 10.3390/econometrics3040797 | |
来源: mdpi | |
【 摘 要 】
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
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190002889ZK.pdf | 370KB | download |