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lassopack: Model selection and prediction with regularized regression in Stata
Open accessAbstract
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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“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]
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Metadata
- Authors
- Ahrens, A., Hansen, C. B., & Schaffer, M. E.
- Affiliations
- Economic and Social Research Institute (IE)
- University of Chicago (US)
- Heriot-Watt University (GB)
- 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
- 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
- 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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