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Feasible generalized least squares using support vector regression

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  • Miller, Steve
  • Startz, Richard

Abstract

We investigate semiparametric Feasible Generalized Least Squares using Support Vector Regression to estimate the conditional variance function. Monte Carlo results indicate the resulting estimator and an accompanying standard error correction offer substantially improved precision, nominal coverage rates, and shorter confidence intervals than Ordinary Least Squares with heteroskedasticity-consistent standard errors. Reductions in root mean squared error can be over 90% of those achievable when the form of heteroskedasticity is known.

Suggested Citation

  • Miller, Steve & Startz, Richard, 2019. "Feasible generalized least squares using support vector regression," Economics Letters, Elsevier, vol. 175(C), pages 28-31.
    • Handle: RePEc:eee:ecolet:v:175:y:2019:i:c:p:28-31
    DOI: 10.1016/j.econlet.2018.12.001
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    References listed on IDEAS

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    More about this item

    Keywords

    Heteroskedasticity; Support vector regression; Weighted regression;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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