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CV_REGRESS: Stata module to estimate the leave-one-out error for linear regression models

Author

Listed:
  • Fernando Rios-Avila

    (Levy Economics Institute of Bard College)

Programming Language

Stata

Abstract

cv_regress uses the shortcut that relies on the leverage statistics to estimate the leave-one-out error, which is typically used in the estimation of Cross-Validation Statistics. For the correct implementation, the OLS model needs to be estimated using -regress- before this program is executed. cv_regress reports three goodness-of-fit measures: the root mean squared error (RMSE), the mean absolute error (MAE), and the pseudo-R2 (the square of the correlation coefficient of the predicted and observed values of the dependent variable). It also gives you the option to save the predicted Leave-one-out error from the model.

Suggested Citation

  • Fernando Rios-Avila, 2018. "CV_REGRESS: Stata module to estimate the leave-one-out error for linear regression models," Statistical Software Components S458469, Boston College Department of Economics, revised 11 Jun 2020.
  • Handle: RePEc:boc:bocode:s458469
    Note: This module may be installed from within Stata by typing "ssc install cv_regress". 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

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

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

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

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