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

Fernando Rios-Avila ()

Statistical Software Components from Boston College Department of Economics

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.

Language: Stata
Requires: Stata version 7
Keywords: cross-validation; leverage; regression (search for similar items in EconPapers)
Date: 2018-03-25, Revised 2020-06-11
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.
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http://fmwww.bc.edu/repec/bocode/c/cv_regress.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/cv_regress.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/c/cv_kfold.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/cv_kfold.sthlp help file (text/plain)

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