Explaining Ridge Regression and LASSO

Katherine Hauck () and Tiemen Woutersen ()
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Katherine Hauck: University of California, Davis
Tiemen Woutersen: University of Arizona

A chapter in Teaching Econometrics, 2026, pp 179-196 from Springer

Abstract: Abstract Machine learning is a tool that uses a computer’s analytic power to make decisions and predictions from data. Two common machine learning techniques are Least Absolute Shrinkage and Selection Operator (LASSO) and Ridge regression. We provide intuition to identify cases in which a researcher may prefer these models to least squares. We discuss the application, implementation, and uses of LASSO and Ridge regression, relative to (i) each other and (ii) least squares, including splitting the data and the choice of tuning parameter. Further, we use an example to compare least squares, LASSO, and Ridge regression to demonstrate how machine learning techniques select the most important regressors for prediction analysis.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:adschp:978-3-031-97942-2_10

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DOI: 10.1007/978-3-031-97942-2_10

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