Lassopack: model selection and prediction with regularized regression in Stata

Ahrens, Achim; Hansen, Christian B.; Schaffer, Mark E.

doi:10.1177/1536867x20909697

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

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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Lasso shrinkage avoids overestimation of coefficients and provides more appropriate (i.e. generalisable) predictions where multiple correlated variables are included in the model.
“Importantly for the present study, shrinkage of the coefficients avoids their overestimation and provides more appropriate (i.e. generalisable) predictions where multiple correlated variables are included in the model”
From Key demographics and psychological skills associated with adjustment to progressive Multiple Sclerosis early in the diagnosis (2022) · cites this paper as [31]
The Introduction (p. 2) says shrinkage tends to improve out-of-sample prediction by reducing overfitting, and the Elastic net section (p. 7) discusses lasso with correlated regressors. But the paper does not say shrinkage avoids overestimation of coefficients; it says shrinkage induces bias, and describes the prediction benefit as a tendency rather than a guarantee. (LLM, conf=0.88)
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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 · 2019: 5
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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