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Explaining Ridge Regression and LASSO

In: Teaching Econometrics

Author

Listed:
  • Katherine Hauck

    (University of California, Davis)

  • Tiemen Woutersen

    (University of Arizona)

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.

Suggested Citation

  • Katherine Hauck & Tiemen Woutersen, 2026. "Explaining Ridge Regression and LASSO," Advanced Studies in Theoretical and Applied Econometrics, in: Eric Hillebrand & William Griffiths (ed.), Teaching Econometrics, pages 179-196, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-97942-2_10
    DOI: 10.1007/978-3-031-97942-2_10
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