Shrinkage Estimation Methods for Vector Autoregressive Models

Vector autoregressive (VAR) model is a fundamental and effective approach for multivariate time series analysis. Shrinkage estimation methods can be applied to high-dimensional VAR models with dimensionality greater than the number of observations, contrary to the standard ordinary least squares method. This package is an integrative package delivering nonparametric, parametric, and semiparametric methods in a unified and consistent manner, such as the multivariate ridge regression in Golub, Heath, and Wahba (1979) , a James-Stein type nonparametric shrinkage method in Opgen-Rhein and Strimmer (2007) , and Bayesian estimation methods using noninformative and informative priors in Lee, Choi, and S.-H. Kim (2016) and Ni and Sun (2005) .


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install.packages("VARshrink")

0.3.1 by Namgil Lee, 2 months ago


https://github.com/namgillee/VARshrink/


Report a bug at https://github.com/namgillee/VARshrink/issues/


Browse source code at https://github.com/cran/VARshrink


Authors: Namgil Lee [aut, cre] , Heon Young Yang [ctb] , Sung-Ho Kim [aut]


Documentation:   PDF Manual  


Task views: Time Series Analysis


GPL-3 license


Imports vars, ars, corpcor, strucchange, stats, MASS, mvtnorm

Suggests knitr, rmarkdown, rticles, kableExtra


See at CRAN