Arbeitspapier

Resurrecting weighted least squares

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.

Sprache
Englisch

Erschienen in
Series: Working Paper ; No. 172

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

Ereignis
Geistige Schöpfung
(wer)
Romano, Joseph P.
Wolf, Michael
Ereignis
Veröffentlichung
(wer)
University of Zurich, Department of Economics
(wo)
Zurich
(wann)
2016

DOI
doi:10.5167/uzh-98546
Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Romano, Joseph P.
  • Wolf, Michael
  • University of Zurich, Department of Economics

Entstanden

  • 2016

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