Weighted Least Squares and Adaptive Least Squares: Further Empirical Evidence

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Authors
Sterchi, Martin
Wolf, Michael
Author (Corporation)
Publication date
2017
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Course of study
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04 - Book part or conference paper
Editors
Kreinovich, Vladik
Sriboonchitta, Songsak
Huynh, Van-Nam
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Parent work
Robustness in Econometrics
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DOI of the original publication
Series
Studies in Computational Intelligence
Series number
692
Volume
Issue / Number
Pages / Duration
135-167
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Publisher / Publishing institution
Springer
Place of publication / Event location
Heidelberg
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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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ISBN
978-3-319-50741-5
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Language
English
Created during FHNW affiliation
Yes
Publication status
Published
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Citation
STERCHI, Martin und Michael WOLF, 2017. Weighted Least Squares and Adaptive Least Squares: Further Empirical Evidence. In: Vladik KREINOVICH, Songsak SRIBOONCHITTA und Van-Nam HUYNH (Hrsg.), Robustness in Econometrics. Heidelberg: Springer. 2017. S. 135–167. Studies in Computational Intelligence, 692. ISBN 978-3-319-50741-5. Verfügbar unter: http://hdl.handle.net/11654/24445