Feasible generalized least squares using support vector regression

Steve Miller, Richard Startz

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)28-31
Number of pages4
JournalEconomics Letters
StatePublished - Feb 2019

Bibliographical note

Funding Information:
We thank Marc Bellemare, Clément de Chaisemartin, Doug Steigerwald, and an anonymous referee for helpful comments and the Minnesota Supercomputing Institute for computational resources.


  • Heteroskedasticity
  • Support vector regression
  • Weighted regression


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