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lassopack: Model selection and prediction with regularized regression in Stata
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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”
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Cites
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
Regression and time series model selection in small samples
HURVICH, C. M., & TSAI, C.-L. (1989) · Biometrika · doi:10.1093/biomet/76.2.297 · Cited by 6,586
Forecasting principles and practice
Hyndman, R. J., & Athanasopoulos, G. (2018) · Cited by 4,405
A survey of cross-validation procedures for model selectionPreprint
Arlot, S., & Célisse, A. (2009) · Cited by 3,627
A survey of cross-validation procedures for model selection
Arlot, S., & Celisse, A. (2010) · Statistics Surveys · doi:10.1214/09-ss054 · Cited by 3,522
The Predictive Sample Reuse Method with Applications
Geisser, S. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10479865 · Cited by 2,748
Statistics for High-Dimensional Data
Bühlmann, P., & van de Geer, S. (2011) · Springer series in statistics · doi:10.1007/978-3-642-20192-9 · Cited by 2,618
High dimensional graphs and variable selection with the Lasso
Meinshausen, N., & Bühlmann, P. (2006) · Cited by 2,539
Statistical Learning with Sparsity
Hastie, T. J., Tibshirani, R. J., & Wainwright, M. J. (2015) · Chapman & Hall/CRC eBooks · doi:10.1201/b18401 · Cited by 2,529
High-dimensional graphs and variable selection with the Lasso
Meinshausen, N., & Bühlmann, P. (2006) · The Annals of Statistics · doi:10.1214/009053606000000281 · Cited by 2,521
Simultaneous analysis of Lasso and Dantzig selector
Bickel, P. J., Ritov, Y., & Tsybakov, A. B. (2009) · The Annals of Statistics · doi:10.1214/08-aos620 · Cited by 2,412
On Model Selection Consistency of Lasso
Zhao, P., & Yu, B. (2006) · doi:10.5555/1248547.1248637 · Cited by 2,314
A Statistical View of Some Chemometrics Regression Tools
Frank, L. E., & Friedman, J. H. (1993) · Technometrics · doi:10.1080/00401706.1993.10485033 · Cited by 2,217
Further analysis of the data by Akaike's information criterion and the finite corrections
Sugiura, N. (1978) · Communication in Statistics- Theory and Methods · doi:10.1080/03610927808827599 · Cited by 2,148
Extended Bayesian information criteria for model selection with large model spaces
Chen, J., & Chen, Z. (2008) · Biometrika · doi:10.1093/biomet/asn034 · Cited by 1,993
Machine Learning: An Applied Econometric Approach
Mullainathan, S., & Spiess, J. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.87 · Cited by 1,989
Pathwise coordinate optimization
Friedman, J. H., Hastie, T. J., Höfling, H., & Tibshirani, R. J. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas131 · Cited by 1,906
Linear Model Selection by Cross-validation
Shao, J. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476299 · Cited by 1,790
Big Data: New Tricks for Econometrics
Varian, H. R. (2014) · The Journal of Economic Perspectives · doi:10.1257/jep.28.2.3 · Cited by 1,588
Statistics for High-Dimensional Data: Methods, Theory and Applications
Bhlmann, P., & van de Geer, S. (2011) · Cited by 1,527
Inference on Treatment Effects after Selection among High-Dimensional Controls
Belloni, A., Chernozhukov, V., & Hansen, C. (2013) · The Review of Economic Studies · doi:10.1093/restud/rdt044 · Cited by 1,516
An asymptotic equivalence of choice of model by cross validation and Akaike's criterion
Stone, M. (1977) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1977.tb01603.x · Cited by 1,286
Penalized Regressions: The Bridge versus the Lasso
Fu, W. J. (1998) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1998.10474784 · Cited by 1,096
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx032 · Cited by 1,040
On the “degrees of freedom” of the lasso
Zou, H., Hastie, T. J., & Tibshirani, R. J. (2007) · The Annals of Statistics · doi:10.1214/009053607000000127 · Cited by 962
A note on the validity of cross-validation for evaluating autoregressive time series prediction
Bergmeir, C., Hyndman, R. J., & Koo, B. (2017) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2017.11.003 · Cited by 929
Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
Belloni, A., Chen, D. L., Chernozhukov, V., & Hansen, C. (2012) · Econometrica · doi:10.3982/ecta9626 · Cited by 892
On the adaptive elastic-net with a diverging number of parameters
Zou, H., & Zhang, H. H. (2009) · The Annals of Statistics · doi:10.1214/08-aos625 · Cited by 854
Least Squares After Model Selection in High-Dimensional Sparse Models
Belloni, A., & Chernozhukov, V. (2009) · Bernoulli · doi:10.3150/11-bej410 · Cited by 694
Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming
Belloni, A., Chernozhukov, V., & Wang, L.-E. (2011) · Biometrika · doi:10.1093/biomet/asr043 · Cited by 635
Yang, Y. (2005) · Biometrika · doi:10.1093/biomet/92.4.937 · Cited by 608
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · doi:10.3386/w23180 · Cited by 545
A significance test for the lasso
Lockhart, R., Taylor, J., Tibshirani, R. J., & Tibshirani, R. (2014) · The Annals of Statistics · doi:10.1214/13-aos1175 · Cited by 541
Adaptive Lasso for sparse high-dimensional regression models
Huang, J., Ma, S., & Zhang, C. H. (2008) · Statistica Sinica · Cited by 517
High-dimensional variable selectionPreprint
Wasserman, L., & Roeder, K. (2009) · Cited by 504
AN ASYMPTOTIC THEORY FOR LINEAR MODEL SELECTION
Shao, J. (1997) · Cited by 503
p -Values for High-Dimensional Regression
Meinshausen, N., Meier, L., & Bühlmann, P. (2009) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.tm08647 · Cited by 447
The Impact of Machine Learning on Economics
Athey, S. C. (2019) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226613475.003.0021 · Cited by 419
High-dimensional variable selection
Wasserman, L., & Roeder, K. (2009) · The Annals of Statistics · doi:10.1214/08-aos646 · Cited by 409
Chernozhukov, V., Chetverikov, D., & Kato, K. (2013) · The Annals of Statistics · doi:10.1214/13-aos1161 · Cited by 325
Chernozhukov, V., Chetverikov, D., & Kato, K. (2013) · doi:10.1920/wp.cem.2013.7613 · Cited by 312
Regularization Parameter Selections via Generalized Information Criterion
Zhang, Y., Li, R., & Tsai, C. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.tm08013 · Cited by 294
A cross-validatory method for dependent data
Burman, P., Chow, E., & Nolan, D. (1994) · Biometrika · doi:10.1093/biomet/81.2.351 · Cited by 245
Degrees of freedom in lasso problems
Tibshirani, R. J., & Taylor, J. (2012) · The Annals of Statistics · doi:10.1214/12-aos1003 · Cited by 239
High-dimensional asymptotics of prediction: Ridge regression and classification
Dobriban, E., & Wager, S. (2018) · The Annals of Statistics · doi:10.1214/17-aos1549 · Cited by 220
Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments
Chernozhukov, V., Hansen, C., & Spindler, M. (2015) · American Economic Review · doi:10.1257/aer.p20151022 · Cited by 211
Statistical significance in high-dimensional linear models
Bühlmann, P. (2013) · Bernoulli · doi:10.3150/12-bejsp11 · Cited by 196
Inference in High-Dimensional Panel Models With an Application to Gun Control
Belloni, A., Chernozhukov, V., Hansen, C., & Kozbur, D. (2015) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2015.1102733 · Cited by 188
Self-normalized Cramér-type large deviations for independent random variables
Jing, B., Shao, Q.-M., & Wang, Q. (2003) · The Annals of Probability · doi:10.1214/aop/1068646382 · Cited by 177
A regularization approach to the many instruments problem
Carrasco, M. (2012) · Journal of Econometrics · doi:10.1016/j.jeconom.2012.05.012 · Cited by 150
Pivotal estimation via square-root Lasso in nonparametric regression
Belloni, A., Chernozhukov, V., & Wang, L.-E. (2014) · The Annals of Statistics · doi:10.1214/14-aos1204 · Cited by 137
Andrews, D. W. K. (1991) · Journal of Econometrics · doi:10.1016/0304-4076(91)90107-o · Cited by 133
The socio-economic determinants of urban sprawl between 1980 and 2010 in Switzerland
Weilenmann, B., Seidl, I., & Schulz, T. (2016) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2016.08.002 · Cited by 123
Instrumental variables estimation with many weak instruments using regularized JIVE
Hansen, C., & Kozbur, D. (2014) · Journal of Econometrics · doi:10.1016/j.jeconom.2014.04.022 · Cited by 110
Random Design Analysis of Ridge Regression
Hsu, D., Kakade, S. M., & Zhang, T. (2014) · Foundations of Computational Mathematics · doi:10.1007/s10208-014-9192-1 · Cited by 96
Pivotal estimation via square-root lasso in nonparametric regression
Belloni, A., Chernozhukov, V., & Wang, L.-E. (2017) · doi:10.1920/wp.cem.2013.6213 · Cited by 88
High Dimensional Sparse Econometric Models: An Introduction
Belloni, A., & Chernozhukov, V. (2011) · Lecture notes in statistics · doi:10.1007/978-3-642-19989-9_3 · Cited by 83
Machine Learning Methods Economists Should Know AboutPreprint
Athey, S. C., & Imbens, G. W. (2019) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1903.10075 · Cited by 65
Ridge regression and asymptotic minimax estimation over spheres of growing dimension
Dicker, L. H. (2015) · Bernoulli · doi:10.3150/14-bej609 · Cited by 44
High Dimensional Sparse Econometric Models: An IntroductionPreprint
Belloni, A., & Chernozhukov, V. (2011) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1106.5242 · Cited by 17
Chetverikov, D. (2017) · doi:10.1920/wp.cem.2016.4716 · Cited by 17
The Frisch–Waugh–Lovell theorem for the lasso and the ridge regression
Yamada, H. (2016) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2016.1252403 · Cited by 14
Scaled and square-root elastic net
Raninen, E., & Ollila, E. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952975 · Cited by 6
High-Dimensional Metrics in RPreprint
Chernozhukov, V., Hansen, C., & Spindler, M. (2016) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1603.01700 · Cited by 3
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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The Origin of the State: Land Productivity or Appropriability?
Mayshar, J., Moav, O., & Pascali, L. (2021) · Journal of Political Economy · doi:10.1086/718372 · Cited by 129
Xu, H., Qin, Y., Hu, G., & Tsang, H. K. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01102-9 · Cited by 94
Zhang, X., Hu, W., Wang, Y., Wang, W., Liao, H., Zhang, X., Kiburg, K., Shang, X., Bulloch, G., Huang, Y., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02449-3 · Cited by 83
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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