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SUBSET: Stata module to implement best covariates and stepwise subset selection

Author

Listed:
  • Giovanni Cerulli

    () (IRCrES-CNR)

Programming Language

Stata

Abstract

subset is a Stata wrapper for the R function "regsubsets()", providing "best", "backward", and "forward" stepwise subset covariates selection, a Machine Learning approach to select the optimal number of features (covariates) in a supervised linear learning approach (i.e. a linear regression model) with many covariates. The "forward" model can be also used when p (the number of covariates) is larger than N (the sample size). This method provides both the optimal subset of covariates for each specific size of the model (i.e., size=1 covariates, size=2 covariates, etc.), and the overall optimal size. The latter one is found using three criteria as validation approaches: Adjusted R2, CP, and BIC.

Suggested Citation

  • Giovanni Cerulli, 2019. "SUBSET: Stata module to implement best covariates and stepwise subset selection," Statistical Software Components S458647, Boston College Department of Economics, revised 23 Mar 2020.
  • Handle: RePEc:boc:bocode:s458647
    Note: This module should be installed from within Stata by typing "ssc install subset". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/s/subset.ado
    File Function: program code
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/s/subset.sthlp
    File Function: help file
    Download Restriction: no
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