This article introduces lassopack, a suite of programs for regularized
regression in Stata. lassopack implements lasso, square-root lasso, elastic
net, ridge regression, adaptive lasso and post-estimation OLS. The methods are
suitable for the high-dimensional setting where the number of predictors p
may be large and possibly greater than the number of observations, n. We
offer three different approaches for selecting the penalization (`tuning')
parameters: information criteria (implemented in lasso2), K-fold
cross-validation and h-step ahead rolling cross-validation for cross-section,
panel and time-series data (cvlasso), and theory-driven (`rigorous')
penalization for the lasso and square-root lasso for cross-section and panel
data (rlasso). We discuss the theoretical framework and practical
considerations for each approach. We also present Monte Carlo results to
compare the performance of the penalization approaches.Comment: 52 pages, 6 figures, 6 tables; submitted to Stata Journal; for more
information see https://statalasso.github.io