An Analytical Shrinkage Estimator for Linear Regression

Nathan Lassance ()
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Nathan Lassance: Université catholique de Louvain, LIDAM/LFIN, Belgium

No 2023003, LIDAM Reprints LFIN from Université catholique de Louvain, Louvain Finance (LFIN)

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)
Pages: 6
Date: 2023-02-01
Note: In: Statistics & Probability Letters, 2023, vol. 194, 109760
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Persistent link: https://EconPapers.repec.org/RePEc:ajf:louvlr:2023003

DOI: 10.1016/j.spl.2022.109760

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