normality.test_sh

An object of class "varshrinkest" obtained by VARshrink().

If TRUE, only the multivariate statistics
is computed.

multivariate.only

This function computes univariate and multivariate Jarque-Bera tests and
multivariate skewness and kurtosis tests for the residuals of a VAR(p)
or of a VECM in levels.
This is a modification of vars::normality.test() for
the class "varshrinkest".

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

normality.test_sh function

<dl><dt>x</dt>
<dd>An object of class "varshrinkest" obtained by VARshrink().</dd>
<dt>multivariate.only</dt>
<dd>If TRUE, only the multivariate statistics
is computed.</dd></dl>

Arguments

Normality, multivariate skewness and kurtosis test — normality.test_sh

<dl>

<dt>x</dt>
<dd>An object of class "varshrinkest" obtained by VARshrink().</dd>


<dt>multivariate.only</dt>
<dd>If TRUE, only the multivariate statistics
is computed.</dd>

</dl>

normality.test_sh: Normality, multivariate skewness and kurtosis test

Description

Usage

Arguments

See Also

Examples