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Tuning parameter selection in econometrics

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  • Denis Chetverikov

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. "Tuning parameter selection in econometrics," Papers 2405.03021, arXiv.org.
    • Handle: RePEc:arx:papers:2405.03021
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