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VARshrink  

Shrinkage Estimation Methods for Vector Autoregressive Models
View on CRAN: Click here


Download and install VARshrink package within the R console
Install from CRAN:
install.packages("VARshrink")

Install from Github:
library("remotes")
install_github("cran/VARshrink")

Install by package version:
library("remotes")
install_version("VARshrink", "0.3.1")



Attach the package and use:
library("VARshrink")
Maintained by
Namgil Lee
[Scholar Profile | Author Map]
First Published: 2019-10-09
Latest Update: 2019-10-09
Description:
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) <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and Bayesian estimation methods using noninformative and informative priors in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and Ni and Sun (2005) <doi:10.1198/073500104000000622>.
How to cite:
Namgil Lee (2019). VARshrink: Shrinkage Estimation Methods for Vector Autoregressive Models. R package version 0.3.1, https://cran.r-project.org/web/packages/VARshrink. Accessed 30 Apr. 2025.
Previous versions and publish date:
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