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Resurrecting weighted least squares


Romano, Joseph P; Wolf, Michael (2017). Resurrecting weighted least squares. Journal of Econometrics, 197(1):1-19.

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 heteroskedasticity-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 Carlo study demonstrates attractive finite-sample properties compared to the status quo, in terms of both estimation and inference.

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 heteroskedasticity-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 Carlo study demonstrates attractive finite-sample properties compared to the status quo, in terms of both estimation and inference.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Uncontrolled Keywords:Conditional heteroskedasticity, HC standard errors, weighted least squares
Language:English
Date:March 2017
Deposited On:16 Dec 2016 08:09
Last Modified:20 Sep 2018 04:16
Publisher:Elsevier
ISSN:0304-4076
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.jeconom.2016.10.003
Related URLs:http://www.sciencedirect.com/science/journal/03044076 (Publisher)

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