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Feasible Generalized Least Squares Using Machine Learning

20 Pages Posted: 10 May 2017 Last revised: 11 May 2017

Steve Miller

University of Minnesota - Twin Cities - Department of Applied Economics

Richard Startz

UCSB

Date Written: May 10, 2017

Abstract

In the presence of heteroskedastic errors, regression using Feasible Generalized Least Squares (FGLS) offers potential efficiency gains over Ordinary Least Squares (OLS). However, FGLS adoption remains limited, in part because the form of heteroskedasticity may be misspecified. We investigate machine learning methods to address this concern, focusing on Support Vector Regression. 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 OLS with heteroskedasticity-consistent (HC3) standard errors. Reductions in root mean squared error are 87-98% of those achievable when the form of heteroskedasticity is known.

Keywords: Heteroskedasticity, Feasible Generalized Least Squares, Machine Learning

JEL Classification: C13, C21

Suggested Citation

Miller, Steve and Startz, Richard, Feasible Generalized Least Squares Using Machine Learning (May 10, 2017). Available at SSRN: https://ssrn.com/abstract=2966194 or http://dx.doi.org/10.2139/ssrn.2966194

Steve Miller (Contact Author)

University of Minnesota - Twin Cities - Department of Applied Economics ( email )

MN
United States

Richard Startz

UCSB ( email )

Department of Economics
University of California
Santa Barbara, CA 93106-9210
United States
805-893-2895 (Phone)

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