{smcl}
{* *! version 1.0.11 4oct2019}{...}
{hline}
{cmd:help lassopack}{right: lassopack v1.3.1}
{hline}
{title:Package}
{p2colset 5 16 18 2}{...}
{p2col:{hi:LASSOPACK}}{p_end}
{p2colreset}{...}
{title:Overview}
{pstd}
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.
{pstd}
The package consists of six main programs:
{pstd}
{helpb 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.
{pstd}
{helpb cvlasso} supports K-fold cross-validation and rolling cross-validation
for cross-section, panel and time-series data.
{pstd}
{helpb 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.
{pstd}
{helpb lassologit}, {helpb cvlassologit} and {helpb rlassologit} are the
corresponding programs for logistic lasso regression.
{pstd}
For more information, please see our website
{browse "https://statalasso.github.io/"},
the help files and our paper below.
{title:Citation of lassopack}
{pstd}{opt lassopack} is not an official Stata package. It is a free contribution
to the research community, like a paper. Please cite it as such: {p_end}
{phang}Ahrens, A., Hansen, C.B., Schaffer, M.E. 2018.
LASSOPACK: Stata module for lasso, square-root lasso, elastic net, ridge, adaptive lasso estimation and cross-validation
{browse "http://ideas.repec.org/c/boc/bocode/s458458.html"}{p_end}
{phang}Ahrens, A., Hansen, C.B., Schaffer, M.E. 2019.
lassopack: Model selection and prediction with regularized regression in Stata
{browse "https://arxiv.org/abs/1901.05397"}{p_end}
{title: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