lassopack: Model selection and prediction with regularized regression in Stata

Achim Ahrens, Christian Hansen and Mark Schaffer ()

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Abstract: 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.

Date: 2019-01
New Economics Papers: this item is included in nep-big and nep-ets
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Related works:
Journal Article: lassopack: Model selection and prediction with regularized regression in Stata (2020)
Working Paper: lassopack: Model Selection and Prediction with Regularized Regression in Stata (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1901.05397

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