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A simulation study is done to compare Ridge Regression (RR) and the Lasso, under the assumption of a linear model, by calculating four metrics: the squared distance, from the true coefficients, of estimated coefficients that are both statistically significant and true; the proportion of true regressors discovered; the squared distance, from the true predictions, of the predictions made using the estimated coefficients that are only statistically significant (but not necessarily true); and the chance that no estimated coefficient is true. Results indicate that RR surpasses the Lasso in regard to all of these metrics. This indicates that RR can add value to model discovery, in Economics and the Sciences, by continuing to employ the key concept of statistical significance in the classical sense to find true regressors. This is important, because it allows the “manufacturer” of models to focus on the  process  generating the data, if indeed there is one. And, thus, provide important feedback on the outputs of other fashionable competitors, such as Machine Learning, with their pervasive “black-box” focus on prediction as an end in itself.

Ridge regression and the Lasso: how do they do as finders of significant regressors and their multipliers?