Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/162410
Authors: 
Romano, Joseph P.
Wolf, Michael
Year of Publication: 
2016
Series/Report no.: 
Working Paper Series, University of Zurich, Department of Economics 172
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.
Subjects: 
Conditional heteroskedasticity
HC standard errors
weighted least squares
JEL: 
C12
C13
C21
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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