The elastic net (Enet) estimator performs penalized least squares regression with a penalty that depends on a parameterized function of the absolute and squared values of the coefficients. Notably, the class of Enet models includes the special cases of ridge and Lasso (Least Absolute Shrinkage and Selection Operator) regression.

We describe here EViews’ tools for estimation and working with elastic net models. EViews allows for estimation given a specific single penalty parameter value or for multiple user-specified or automatically determined penalty values. When multiple penalties are employed, EViews provides cross-validation tools for choosing an optimal value.

Following estimation, EViews offers specialized views of tables of the coefficients and other summary statistics, graphs of coefficient evolution with respect to the penalty parameter and other statistics, and diagnostics for cross validation.

The following discussion includes brief background for elastic net, Lasso, and ridge regression methods. Detailed discussions may be found in Zou and Hastie (2005), Friedman, Hastie, and Tibshirani. (2010), and the textbook by Friedman, Hastie, and Tibshirani (2009).