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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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    References listed on IDEAS

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    1. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    2. Rong Zhu & Alan T. K. Wan & Xinyu Zhang & Guohua Zou, 2019. "A Mallows-Type Model Averaging Estimator for the Varying-Coefficient Partially Linear Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 882-892, April.
    3. Rong Zhu & Haiying Wang & Xinyu Zhang & Hua Liang, 2023. "A Scalable Frequentist Model Averaging Method," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1228-1237, October.
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