Resurrecting Weighted Least Squares

University of Zurich, Department of Economics, Working Paper No. 172, Revised version

49 Pages Posted: 5 Sep 2014 Last revised: 27 Oct 2016

See all articles by Joseph P. Romano

Joseph P. Romano

Stanford University - Department of Statistics

Michael Wolf

University of Zurich - Department of Economics

Date Written: October 2016

Abstract

This paper shows how asymptotically valid inference in regression models based on the weighted least squares (WLS) estimator can be obtained even when the model for reweighting the data is misspecified. Like the ordinary least squares estimator, the WLS estimator can be accompanied by heterokedasticty-consistent (HC) standard errors without knowledge of the functional form of conditional heteroskedasticity. First, we provide rigorous proofs under reasonable assumptions; second, we provide numerical support in favor of this approach. Indeed, a Monte Carly study demonstrates attractive finite-sample properties compared to the status quo, both in terms of estimation and making inference.

Keywords: Conditional heteroskedasticity, HC standard errors, weighted least squares

JEL Classification: C12, C13, C21

Suggested Citation

Romano, Joseph P. and Wolf, Michael, Resurrecting Weighted Least Squares (October 2016). University of Zurich, Department of Economics, Working Paper No. 172, Revised version, Available at SSRN: https://ssrn.com/abstract=2491081 or http://dx.doi.org/10.2139/ssrn.2491081

Joseph P. Romano (Contact Author)

Stanford University - Department of Statistics ( email )

Stanford, CA 94305
United States

Michael Wolf

University of Zurich - Department of Economics ( email )

Wilfriedstrasse 6
Zurich, 8032
Switzerland

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