Cited by 213Statistical Methods and InferenceAdvanced Statistical Methods and ModelsSpatial and Panel Data Analysis

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lassopack: Model selection and prediction with regularized regression in Stata

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Ahrens, Achim, Hansen, Christian B., Schaffer, Mark E.·2020·The Stata Journal Promoting communications on statistics and Stata

DOI 10.1177/1536867x20909697

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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”) 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” or plugin) 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 performances of the penalization approaches.

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Authors: Ahrens, A., Hansen, C. B., & Schaffer, M. E.
Affiliations: Economic and Social Research Institute (IE)
University of Chicago (US)
Heriot-Watt University (GB)
ORCID: Ahrens, Achim
Hansen, Christian B.
Schaffer, Mark E.
Venue: The Stata Journal Promoting communications on statistics and Stata
Publisher: SAGE Publications
Published: 2020-03
Volume: 20
Issue: 1
Pages: 176-235
ISSN: 1536-867X (ISSN-L)
1536-8734
DOI: 10.1177/1536867x20909697
OpenAlex: W2910950436
Cited by: 212
References: 75
Citations by year: 2026: 19 · 2025: 36 · 2024: 35 · 2023: 33 · 2022: 30 · 2021: 35 · 2020: 19
FWCI: 20.79
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: The Stata Journal Promoting communications on statistics and Stata (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
arXiv (Cornell University) (submittedVersion)
AgEcon Search (University of Minnesota, USA) (submittedVersion, cc-by-nc-nd)
arXiv (Cornell University)
AgEcon Search (University of Minnesota, USA)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Keywords: penalized regression, LASSO, square-root Lasso, elastic net, ridge regression, adaptive LASSO, high-dimensional data, model selection, hyperparameter optimization, information criteria, cross-validation, postestimation, Stata software

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