A Working Paper entitled lassopack: Model selection and prediction with regularized regression in Stata is now available.
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 may be large and possibly greater than the number of observations, .
We offer three different approaches for selecting the penalization (‘tuning’) parameters:
information criteria (implemented in
lasso2), -fold cross-validation and -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 (
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.
Ahrens, A., Hansen, C.B., & Schaffer, M.E. (2019). lassopack: Model selection and prediction with regularized regression in Stata. arXiv:1901.05397.