With thanks to Kit Baum, two new user-written packages by Achim Ahrens, Chris Hansen and Mark Schaffer are now available through the SSC archive: LASSOPACK and PDSLASSO.
LASSOPACK is a suite of programs for penalized regression methods: the lasso, square-root lasso, adaptive lasso, elastic net, ridge regression and post-estimation OLS. These methods are suitable for the high-dimensional setting where the number of predictors may be large and possibly greater than the number of observations.
PDSLASSO implements routines for estimating structural parameters in linear models with many controls and/or many instruments. A VCV for the estimated coefficients is also reported, making inference/testing possible. PDSLASSO makes use of the lasso and square-root lasso to select controls and/or instruments from a large set of variables (possibly numbering more than the number of observations), in a setting where the researcher is interested in estimating the causal impact of one or more (possibly endogenous) causal variables of interest.
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 L1 norm penalization to achieve sparse solutions: of the full set of predictors, typically most will have coefficients set to zero. Ridge regression (Hoerl & Kennard 1970) relies on L2 norm penalization; the elastic net (Zou & Hastie 2005) uses a mix of L1 and L2 penalization.
LASSOPACK consists of three main programs:
The PDSLASSO package includes two commands:
For an alternative Stata package to estimate the elastic net, lasso and ridge regression using coordinate descent, see elasticregress by Wilbur Townsend, announced on Statalist here:
https://www.statalist.org/forums/for...net-regression
LASSOPACK is a suite of programs for penalized regression methods: the lasso, square-root lasso, adaptive lasso, elastic net, ridge regression and post-estimation OLS. These methods are suitable for the high-dimensional setting where the number of predictors may be large and possibly greater than the number of observations.
PDSLASSO implements routines for estimating structural parameters in linear models with many controls and/or many instruments. A VCV for the estimated coefficients is also reported, making inference/testing possible. PDSLASSO makes use of the lasso and square-root lasso to select controls and/or instruments from a large set of variables (possibly numbering more than the number of observations), in a setting where the researcher is interested in estimating the causal impact of one or more (possibly endogenous) causal variables of interest.
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 L1 norm penalization to achieve sparse solutions: of the full set of predictors, typically most will have coefficients set to zero. Ridge regression (Hoerl & Kennard 1970) relies on L2 norm penalization; the elastic net (Zou & Hastie 2005) uses a mix of L1 and L2 penalization.
LASSOPACK consists of three main programs:
- lasso2 implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS.
- cvlasso supports K-fold cross-validation and rolling h-step ahead cross-validation (for time-series and panel data) to choose the optimal tuning parameters, i.e., the overall penalty level, lambda, and the elastic net parameter, alpha.
- rlasso implements theory-driven penalization for the lasso and square-root lasso for cross-section and panel data based on theory developed in Belloni et al. (2012, 2013, 2014, 2016). In addition, rlasso can also report the Chernozhukov et al. (2013) sup-score test of joint significance of the regressors, a test that is suitable for the high-dimensional setting.
The PDSLASSO package includes two commands:
- pdslasso, which allows for estimating structural parameters in linear models with many controls.
- ivlasso, which in addition allows for endogenous treatment variables and many instruments.
For an alternative Stata package to estimate the elastic net, lasso and ridge regression using coordinate descent, see elasticregress by Wilbur Townsend, announced on Statalist here:
https://www.statalist.org/forums/for...net-regression
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