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>.