predict.varshrinkest

An object of class 'varshrinkest';
generated by <code>VARshrink()</code>

object

An integer specifying the number of forecast steps.

n.ahead

Matrix for objects of class &lt;U+2018&gt;vec2var&lt;U+2019&gt; or &lt;U+2018&gt;varest&lt;U+2019&gt;,
if the dumvar argument in ca.jo() has been used or if the exogen
argument in VARshrink() has been used, respectively. The matrix should
have the same column dimension as in the call to ca.jo() or to
VARshrink() and row dimension equal to n.ahead.

dumvar

Forecating a VAR object of class 'varshrinkest'
with confidence bands.
This is a modification of vars::predict.varest() 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>.

predict.varshrinkest: Predict method for objects of class varshrinkest

Description

Usage

Arguments