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