Inference for Support Vector Regression under ℓ1 Regularization

Yuehao Bai, Hung Ho, Guillaume A. Pouliot and Joshua Shea

AEA Papers and Proceedings, 2021, vol. 111, 611-15

Abstract: We provide large-sample distribution theory for support vector regression (SVR) with ℓ1-norm along with error bars for the SVR regression coefficients. Although a classical Wald confidence interval obtains from our theory, its implementation inherently depends on the choice of a tuning parameter that scales the variance estimate and thus the width of the error bars. We address this shortcoming by further proposing an alternative large-sample inference method based on the inversion of a novel test statistic that displays competitive power properties and does not depend on the choice of a tuning parameter.

JEL-codes: C32 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1257/pandp.20211035

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