fevd.varshrinkest

<p>Object of class 'varshrinkest';
generated by <code>VARshrink()</code>.</p>

n.ahead

<p>Computes the forecast error variance decomposition of a VAR(p) for
n.ahead steps.
This is a modification of vars::fevd() for the class "varshrinkest".</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

fevd.varshrinkest: Forecast Error Variance Decomposition

Description

Usage

Arguments

See Also