Ridge Regression

This is a way of using Bayesian models in a VAR framework. Prior to Lasso, the most widely used method for choosing which variables to include was stepwise selection. At that time, ridge regression was the most popular alternative technique used for improving prediction accuracy. Ridge regression improves prediction error by shrinking large regression coefficients in order to reduce overfitting, but it does not perform variable selection and therefore does not help to make the model more interpretable.