Tuning parameter selection in econometrics

Denis Chetverikov

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arXiv · 2405.03021

Tuning parameter selection in econometrics

Denis Chetverikov

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Abstract

I review some of the main methods for selecting tuning parameters in nonparametric and $\ell_1$-penalized estimation. For the nonparametric estimation, I consider the methods of Mallows, Stein, Lepski, cross-validation, penalization, and aggregation in the context of series estimation. For the $\ell_1$-penalized estimation, I consider the methods based on the theory of self-normalized moderate deviations, bootstrap, Stein's unbiased risk estimation, and cross-validation in the context of Lasso estimation. I explain the intuition behind each of the methods and discuss their comparative advantages. I also give some extensions.

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Denis Chetverikov. 2024-05-05. Tuning parameter selection in econometrics. https://arxiv.org/abs/2405.03021

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