Elastic Net Regression in EViews
Posted: Mon Sep 16, 2013 2:27 am
Hi,
Is there any way to implement the elastic net (a combination of the Lasso and Ridge regressions) in EViews, in which some variables are shrunken to 0 so that they are dropped from the model?
The estimation uses the objective function: min L(Beta) = the sum of( (y - X * Beta)^2 + lambda * P(Beta) ), in which the value of lambda is found using cross validation.
Thanks in advance!
Is there any way to implement the elastic net (a combination of the Lasso and Ridge regressions) in EViews, in which some variables are shrunken to 0 so that they are dropped from the model?
The estimation uses the objective function: min L(Beta) = the sum of( (y - X * Beta)^2 + lambda * P(Beta) ), in which the value of lambda is found using cross validation.
Thanks in advance!