Resurrecting weighted least squares

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 172

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Estimation: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
• Subject: Conditional heteroskedasticity ; HC standard errors ; weighted least squares

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Romano, Joseph P. ; Wolf, Michael
• Event: Veröffentlichung
• (who): University of Zurich, Department of Economics
• (where): Zurich
• (when): 2016

Further information

• DOI: doi:10.5167/uzh-98546
• Handle: http://hdl.handle.net/10419/162410
• Last update: 10.03.2025, 11:43 AM CET