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A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) <doi:10.1016/j.jspi.2007.11.003>.
Package details |
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Author | Echo Liu [aut, cre], Daniel Xia [aut], R. Douglas Martin [aut] |
Maintainer | Echo Liu <yuhong.echo.liu@gmail.com> |
License | MIT + file LICENSE |
Version | 0.4.2 |
URL | https://github.com/EchoRLiu/robustGarch |
Package repository | View on CRAN |
Installation |
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