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
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: Suggested Citation