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Inference for Support Vector Regression under ℓ1 Regularization

Author

Listed:
  • Yuehao Bai
  • Hung Ho
  • Guillaume A. Pouliot
  • Joshua Shea

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.

Suggested Citation

  • Yuehao Bai & Hung Ho & Guillaume A. Pouliot & Joshua Shea, 2021. "Inference for Support Vector Regression under ℓ1 Regularization," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 611-615, May.
    • Handle: RePEc:aea:apandp:v:111:y:2021:p:611-15
    DOI: 10.1257/pandp.20211035
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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