lassopack: Model selection and prediction with regularized regression in Stata

Achim Ahrens, Christian Hansen and Mark Schaffer ()

Stata Journal, 2020, vol. 20, issue 1, 176-235

Abstract: In this article, we introduce lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso, and postestimation ordinary least squares. 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 approaches for selecting the penalization (“tuning”) parame- ters: 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” or plugin) penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theo- retical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performances of the penalization approaches.

Keywords: lasso2; cvlasso; rlasso; cvlassologit; lassologit; rlassologit; lasso2 postestimation; lassologit postestimation; rlasso postestimation; lasso; elastic net; square-root lasso; cross-validation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (57)

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http://hdl.handle.net/10.1177/1536867X20909697

Related works:
Working Paper: lassopack: Model selection and prediction with regularized regression in Stata (2019)
Working Paper: lassopack: Model Selection and Prediction with Regularized Regression in Stata (2019)
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DOI: 10.1177/1536867X20909697

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