lm_ShVAR_KCV: K-fold Cross Validation for Selection of Shrinkage Parameters...
In VARshrink: Shrinkage Estimation Methods for Vector Autoregressive Models

Description Usage Arguments Details References

Estimate regression coefficients and scale matrix for noise by using semiparametric Bayesian shrinkage estimator, whose shrinkage parameters are selected by K-fold cross validation (KCV).

1 2	lm_ShVAR_KCV(Y, X, dof = Inf, lambda = NULL, lambda_var = NULL, prior_type = c("NCJ", "CJ"), num_folds = 5, m0 = ncol(Y))

`Y`	An N x K matrix of dependent variables.
`X`	An N x M matrix of regressors.
`dof`	Degree of freedom for multivariate t-distribution. If dof = Inf (default), then multivariate normal distribution is applied and weight vector q is not estimated. If dof = NULL or a numeric vector, then dof is selected by K-fold CV automatically and q is estimated.
`lambda`	If NULL or a vector of length >=2, it is selected by KCV.
`lambda_var`	If NULL or a vector of length >=2, it is selected by KCV.
`prior_type`	"NCJ" for non-conjugate prior and "CJ" for conjugate prior for scale matrix Sigma.
`num_folds`	Number of folds for KCV.
`m0`	A hyperparameter for inverse Wishart distribution for Sigma

The shrinkage parameters, lambda and lambda_var, for the semiparametric Bayesian shrinkage estimator are selected by KCV. See help(lm_semi_Bayes_PCV) for details about semiparametric Bayesian estimator.

N. Lee, H. Choi, and S.-H. Kim (2016). Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise. Computational Statistics & Data Analysis 101, 250-276. doi: 10.1016/j.csda.2016.03.007

VARshrink documentation built on Oct. 9, 2019, 5:06 p.m.