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Weighted least squares and adaptive least squares: further empirical evidence


Sterchi, Martin; Wolf, Michael (2017). Weighted least squares and adaptive least squares: further empirical evidence. In: Kreinovič, Vladik; Sriboonchitta, Songsak; Huynh, Van-Nam. Robustness in Econometrics. Cham: Springer, 135-167.

Abstract

This paper compares ordinary least squares (OLS), weighted least squares (WLS), and adaptive least squares (ALS) by means of a Monte Carlo study and an application to two empirical data sets. Overall, ALS emerges as the winner: It achieves most or even all of the efficiency gains of WLS over OLS when WLS outperforms OLS, but it only has very limited downside risk compared to OLS when OLS outperforms WLS.

Abstract

This paper compares ordinary least squares (OLS), weighted least squares (WLS), and adaptive least squares (ALS) by means of a Monte Carlo study and an application to two empirical data sets. Overall, ALS emerges as the winner: It achieves most or even all of the efficiency gains of WLS over OLS when WLS outperforms OLS, but it only has very limited downside risk compared to OLS when OLS outperforms WLS.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Uncontrolled Keywords:Computational Intelligence, artificial Intelligence (incl. robotics), econometrics
Scope:Discipline-based scholarship (basic research)
Language:English
Date:2017
Deposited On:14 Mar 2017 09:50
Last Modified:18 May 2024 03:44
Publisher:Springer
Series Name:Studies in Computational Intelligence
Number:692
ISSN:1860-949X
ISBN:978-3-319-50741-5
OA Status:Green
Publisher DOI:https://doi.org/10.1007/978-3-319-50742-2_9
Other Identification Number:merlin-id:14678