An analytical shrinkage estimator for linear regression

Nathan Lassance

Statistics & Probability Letters, 2023, vol. 194, issue C

Abstract: We derive an analytical solution to the optimal shrinkage of OLS regression coefficients toward a constant target, under any first two moments of predictors. The estimator closely mimics the prediction performance of ridge penalty, which admits no general analytical solution.

Keywords: Linear regression; Prediction error; Shrinkage; Out-of-sample (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.spl.2022.109760

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