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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A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648
The adaptive lasso and its oracle properties
Zou, H. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000735 · Cited by 7,470
Ridge Regression: Biased Estimation for Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.2307/1271436 · Cited by 7,001

Show all 75 references

Research works printed but not linked: 6

Huang J., 2008, Statistica Sinica, 18, 1603
Shao J., 1997, Statistica Sinica, 7, 221
Tikhonov A. N., 1963, Doklady Akademii Nauk, 151, 501
Van der Kooij A., 2007, PhD thesis
Yang Y., 2006, Statistica Sinica, 16, 635
Zhao P., 2006, Journal of Machine Learning Research, 7, 2541

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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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lassopack: Model selection and prediction with regularized regression in Stata

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