lasso2 implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The lasso (Least Absolute Shrinkage and Selection Operator, Tibshirani 1996), the square-root-lasso (Belloni et al. 2011) and the adaptive lasso (Zou 2006) are regularization methods that use
\(\ell_1\)
norm penalization to achieve sparse solutions: of the full set of
\(p\)
predictors, typically most will have coefficients set to zero. Ridge regression (Hoerl & Kennard 1970) relies on
\(\ell_2\)
norm penalization; the elastic net (Zou & Hastie 2005) uses a mix of
\(\ell_1\)
and
\(\ell_2\)
penalization.

cvlasso supports
\(K\)
-fold cross-validation and h-step ahead rolling cross-validation (for time-series and panel data) to choose the penalization parameters for all the implemented estimators.

rlasso implements theory-driven penalization for the lasso and square-root lasso that can be applied to cross-section and panel data. rlasso uses the theory-driven penalization methodology of Belloni et al. (2012, 2013, 2014, 2016) for the lasso and square-root lasso. In addition, rlasso implements the Chernozhukov et al. (2013) sup-score test of joint significance of the regressors that is suitable for the high-dimensional setting.

lassologit, cvlassologit and rlassologit for logistic regression.