*! version 1.0.12 31july2020

help lassopack lassopack v1.4.2

Package

LASSOPACK

Overview

LASSOPACK is a suite of programs for penalized regression methods suitable
for the high-dimensional setting where the number of predictors p may be
large and possibly greater than the number of observations.

The package consists of six main programs:

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 L1 norm penalization to achieve sparse solutions:
of the full set of p predictors, typically most will have coefficients set to
Ridge regression (Hoerl & Kennard 1970) relies on L2 norm penalization;
the elastic net (Zou & Hastie 2005) uses a mix of L1 and L2 penalization.

cvlasso supports K-fold cross-validation and rolling cross-validation
for cross-section, panel and time-series data.

rlasso implements theory-driven penalization for the lasso and square-root
lasso for 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.

lassologit, cvlassologit and rlassologit are the
corresponding programs for logistic lasso regression.

For more information, please see our website
https://statalasso.github.io/,
the help files and our paper below.

Citation of lassopack

lassopack is not an official Stata package. It is a free contribution
to the research community, like a paper. Please cite it as such:

Ahrens, A., Hansen, C.B., Schaffer, M.E. 2018 (updated 2020).
LASSOPACK: Stata module for lasso, square-root lasso, elastic net, ridge, adaptive lasso estimation and cross-validation
http://ideas.repec.org/c/boc/bocode/s458458.html

Ahrens, A., Hansen, C.B. and M.E. Schaffer. 2020.
lassopack: model selection and prediction with regularized regression in Stata.
The Stata Journal, 20(1):176-235.
https://journals.sagepub.com/doi/abs/10.1177/1536867X20909697.
Working paper version: https://arxiv.org/abs/1901.05397.

Authors

Achim Ahrens, Public Policy Group, ETH Zurich, Switzerland
achim.ahrens@gess.ethz.ch

Christian B. Hansen, University of Chicago, USA
Christian.Hansen@chicagobooth.edu

Mark E. Schaffer, Heriot-Watt University, UK
m.e.schaffer@hw.ac.uk