shrinkVARcoef

An N x K matrix of dependent variables.

A shrinkage intensity parameter value between 0~1.

lambda

Degree of freedom for multivariate t-distribution.
If NULL or Inf, then use multivariate normal distribution.

"NCJ" for non-conjugate prior and "CJ" for conjugate
prior for scale matrix Sigma.

prior_type

Tolerance parameter for stopping criterion.

TolDRes

A hyperparameter for inverse Wishart distribution for Sigma

Compute the semiparametric Bayesian shrinkage estimator of Psi and Sigma
for a given shrinkage parameter lambda.
The function is a private function for lm_semi_Bayes_PCV() and
lm_ShVAR_KCV().

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>.

shrinkVARcoef: Semiparametric Bayesian Shrinkage Estimator for Multivariate Regression

Description

Usage

Arguments

References