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LASSOPACK: Stata module for lasso, square-root lasso, elastic net, ridge, adaptive lasso estimation and cross-validation

Author

Listed:
  • Achim Ahrens

    () (Economic and Social Research Institute, Republic of Ireland)

  • Christian B. Hansen

    () (University of Chicago)

  • Mark E Schaffer

    () (Heriot-Watt University)

Abstract

lassopack is a suite of programs for penalized regression methods suitable for the high-dimensional setting where the number of predictors p may be large and possibly greater than the number of observations. The package consists of three main programs: lasso2 implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. cvlasso supports K-fold cross-validation and rolling cross-validation for cross-section, panel and time-series data. rlasso implements theory-driven penalization for the lasso and square-root lasso for cross-section and panel data. The lasso (Least Absolute Shrinkage and Selection Operator, Tibshirani 1996), the square-root-lasso (Belloni et al. 2011) and the adaptive lasso (Zou 2006) are regularization methods that use L1 norm penalization to achieve sparse solutions: of the full set of p predictors, typically most will have coefficients set to zero. Ridge regression (Hoerl & Kennard 1970) relies on L2 norm penalization; the elastic net (Zou & Hastie 2005) uses a mix of L1 and L2 penalization. lasso2 implements all these estimators. rlasso uses the theory-driven penalization methodology of Belloni et al. (2012, 2013, 2014, 2016) for the lasso and square-root lasso. cvlasso implements K-fold cross-validation and h-step ahead rolling cross-validation (for time-series and panel data) to choose the penalization parameters for all the implemented estimators. In addition, rlasso implements the Chernozhukov et al. (2013) sup-score test of joint significance of the regressors that is suitable for the high-dimensional setting.

Suggested Citation

  • Achim Ahrens & Christian B. Hansen & Mark E Schaffer, 2018. "LASSOPACK: Stata module for lasso, square-root lasso, elastic net, ridge, adaptive lasso estimation and cross-validation," Statistical Software Components S458458, Boston College Department of Economics, revised 19 Sep 2018.
  • Handle: RePEc:boc:bocode:s458458
    Note: This module should be installed from within Stata by typing "ssc install lassopack". Windows users should not attempt to download these files with a web browser.
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    File URL: http://fmwww.bc.edu/repec/bocode/l/lassoutils.ado
    File Function: program code
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/l/lasso2.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/l/lasso2_p.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/l/lasso2.sthlp
    File Function: help file
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    File URL: http://fmwww.bc.edu/repec/bocode/c/cvlasso.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/c/cvlasso.sthlp
    File Function: help file
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    File URL: http://fmwww.bc.edu/repec/bocode/r/rlasso.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/r/rlasso_p.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/r/rlasso.sthlp
    File Function: help file
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    File URL: http://fmwww.bc.edu/repec/bocode/c/cs_rlasso.do
    File Function: certification script
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    File URL: http://fmwww.bc.edu/repec/bocode/c/cs_cvlasso.do
    File Function: certification script
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    File URL: http://fmwww.bc.edu/repec/bocode/c/cs_lasso2.do
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    Cited by:

    1. Mozaffari, Samaneh & Nateghi, Mohammad Reza & Zarandi, Mahmood Borhani, 2017. "An overview of the Challenges in the commercialization of dye sensitized solar cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 675-686.

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