Feasible generalized least squares using support vector regression

Steve Miller and Richard Startz

Economics Letters, 2019, vol. 175, issue C, 28-31

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.

Keywords: Heteroskedasticity; Support vector regression; Weighted regression (search for similar items in EconPapers)
JEL-codes: C14 C21 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:175:y:2019:i:c:p:28-31

DOI: 10.1016/j.econlet.2018.12.001

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