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dc.contributor.authorSterchi, Martin
dc.contributor.authorWolf, Michael
dc.contributor.editorKreinovich, Vladik
dc.contributor.editorSriboonchitta, Songsak
dc.contributor.editorHuynh, Van-Nam
dc.date.accessioned2017-02-22T15:36:44Z
dc.date.available2017-04-19T13:18:18Z
dc.date.issued2017
dc.identifier.isbn978-3-319-50741-5
dc.identifier.urihttp://hdl.handle.net/11654/24445
dc.description.abstractThis 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.
dc.description.urihttp://link.springer.com/chapter/10.1007/978-3-319-50742-2_9
dc.language.isoen_US
dc.publisherSpringer
dc.relation.ispartofRobustness in Econometrics
dc.relation.ispartofseriesStudies in Computational Intelligence;692
dc.accessRightsAnonymous
dc.titleWeighted Least Squares and Adaptive Least Squares: Further Empirical Evidence
dc.type04 - Beitrag Sammelband oder Konferenzschrift
dc.volume692
dc.spatialHeidelberg
dc.audienceScience
fhnw.publicationStatePublished
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.InventedHereYes
fhnw.PublishedSwitzerlandNo
fhnw.pagination135-167
fhnw.IsStudentsWorkno


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