文章基本信息

标题：garchx: Flexible and Robust GARCH-X Modeling
本地全文：下载
作者：Genaro Sucarrat
期刊名称：R News
印刷版ISSN：1609-3631
出版年度：2021
卷号：13
期号：1
页码：276-291
语种：English
出版社：The R Foundation for Statistical Computing
摘要：The garchx package provides a user-friendly, fast, flexible, and robust framework for the estimation and inference of GARCH(p, q, r)-X models, where p is the ARCH order, q is the GARCH order, r is the asymmetry or leverage order, and ’X’ indicates that covariates can be included. Quasi Maximum Likelihood (QML) methods ensure estimates are consistent and standard errors valid, even when the standardized innovations are non-normal or dependent, or both. Zero-coefficient restrictions by omission enable parsimonious specifications, and functions to facilitate the non-standard inference associated with zero-restrictions in the null-hypothesis are provided. Finally, in the formal comparisons of precision and speed, the garchx package performs well relative to other prominent GARCH-packages on CRAN.