VARshrink: 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) <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>.

Version: 0.3.1
Depends: R (≥ 3.5.0)
Imports: vars (≥ 1.5.3), ars (≥ 0.6), corpcor (≥ 1.6.9), strucchange, stats, MASS, mvtnorm
Suggests: knitr, rmarkdown, rticles, kableExtra
Published: 2019-10-09
DOI: 10.32614/CRAN.package.VARshrink
Author: Namgil Lee ORCID iD [aut, cre], Heon Young Yang [ctb], Sung-Ho Kim [aut]
Maintainer: Namgil Lee <namgil.lee at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
In views: TimeSeries
CRAN checks: VARshrink results


Reference manual: VARshrink.pdf
Vignettes: Article for VARshrink


Package source: VARshrink_0.3.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): VARshrink_0.3.1.tgz, r-oldrel (arm64): VARshrink_0.3.1.tgz, r-release (x86_64): VARshrink_0.3.1.tgz, r-oldrel (x86_64): VARshrink_0.3.1.tgz


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