{smcl}

{* *! version 1.0.12 31july2020}{...}

{hline}

{cmd:help lassopack}{right: lassopack v1.4.3}

{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 (updated 2020).

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. and M.E. Schaffer. 2020.

lassopack: model selection and prediction with regularized regression in Stata.

{it:The Stata Journal}, 20(1):176-235.

{browse "https://journals.sagepub.com/doi/abs/10.1177/1536867X20909697"}.

Working paper version: {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