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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Resurrecting weighted least squares
Organization Unit
  • Joseph P Romano
  • Michael Wolf
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 172
ISSN 1664-7041
Number of Pages 48
Date 2016
Abstract Text 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.
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Keywords Conditional heteroskedasticity, HC standard errors, weighted least squares, ökonometrisches Modell, gewichtete Methode der kleinsten Quadrate, Heteroskedastizität
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