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An Upper Bound for Functions of Estimators in High Dimensions

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  • Mehmet Caner
  • Xu Han

Abstract

We provide an upper bound as a random variable for the functions of estimators in high dimensions. This upper bound may help establish the rate of convergence of functions in high dimensions. The upper bound random variable may converge faster, slower, or at the same rate as estimators depending on the behavior of the partial derivative of the function. We illustrate this via three examples. The first two examples use the upper bound for testing in high dimensions, and third example derives the estimated out-of-sample variance of large portfolios. All our results allow for a larger number of parameters, p, than the sample size, n.

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  • Mehmet Caner & Xu Han, 2020. "An Upper Bound for Functions of Estimators in High Dimensions," Papers 2008.02636, arXiv.org.
    • Handle: RePEc:arx:papers:2008.02636
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    References listed on IDEAS

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    1. Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2015. "Risks of large portfolios," Journal of Econometrics, Elsevier, vol. 186(2), pages 367-387.
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    5. Fan, Jianqing & Han, Fang & Liu, Han & Vickers, Byron, 2016. "Robust inference of risks of large portfolios," Journal of Econometrics, Elsevier, vol. 194(2), pages 298-308.
    6. Caner, Mehmet, 2014. "Near exogeneity and weak identification in generalized empirical likelihood estimators: Many moment asymptotics," Journal of Econometrics, Elsevier, vol. 182(2), pages 247-268.
    7. Laurent Callot & Mehmet Caner & A. Özlem Önder & Esra Ulaşan, 2021. "A Nodewise Regression Approach to Estimating Large Portfolios," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 520-531, March.
    8. Mengmeng Ao & Li Yingying & Xinghua Zheng, 2019. "Approaching Mean-Variance Efficiency for Large Portfolios," The Review of Financial Studies, Society for Financial Studies, vol. 32(7), pages 2890-2919.
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