lm_multiv_ridge: Multivariate Ridge Regression

In VARshrink: Shrinkage Estimation Methods for Vector Autoregressive Models

Description

Estimate regression coefficients by using ridge regression.

Usage

lm_multiv_ridge(Y, X, lambda = 0, do_scale = FALSE)

Arguments

Y	An N x K matrix of dependent variables.
X	An N x M matrix of regressors.
lambda	Numeric vector of lambda values
do_scale	If true, X is centered and scaled, and Y is centered.

Details

Consider the multivariate regression:

Y = X Psi + e.

Psi is a M-by-K matrix of regression coefficients. The ridge regression estimate for the coefficients is

Psi = (X'X + lambda * I)^{-1} X'Y.

Value

A list object with the components: 1) Psi - A list of estimated Psi matrices, 2) lambda - A vector of lambda values, 3) GCV - A vector of GCV values

References

G. H. Golub, M. Heath, G. Wahba (1979). Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21(2), 215-223. doi: 10.2307/1268518

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