roots_sh

modulus

<p>This is a variant of vars::roots() for an object of class 'varshrinkest',
VAR parameters estimated by <code>VARshrink()</code>.</p>


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>.

Namgil Lee

VARshrink

Shrinkage Estimation Methods for Vector Autoregressive Models

Heon Young Yang

Sung-Ho Kim

roots_sh function

Eigenvalues of the companion coefficient matrix of
a VAR(p)-process — roots_sh

Eigenvalues of the companion coefficient matrix of
a VAR(p)-process

roots_sh: Eigenvalues of the companion coefficient matrix of a VAR(p)-process

Description

Usage

Arguments

See Also

Examples