Weighted Least Squares and Adaptive Least Squares: Further Empirical Evidence
| dc.accessRights | Anonymous | |
| dc.audience | Science | |
| dc.contributor.author | Sterchi, Martin | |
| dc.contributor.author | Wolf, Michael | |
| dc.contributor.editor | Kreinovich, Vladik | |
| dc.contributor.editor | Sriboonchitta, Songsak | |
| dc.contributor.editor | Huynh, Van-Nam | |
| dc.date.accessioned | 2017-02-22T15:36:44Z | |
| dc.date.available | 2017-04-19T13:18:18Z | |
| dc.date.issued | 2017 | |
| dc.description.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. | |
| dc.description.uri | http://link.springer.com/chapter/10.1007/978-3-319-50742-2_9 | |
| dc.identifier.isbn | 978-3-319-50741-5 | |
| dc.identifier.uri | http://hdl.handle.net/11654/24445 | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | |
| dc.relation.ispartof | Robustness in Econometrics | |
| dc.relation.ispartofseries | Studies in Computational Intelligence | |
| dc.spatial | Heidelberg | |
| dc.title | Weighted Least Squares and Adaptive Least Squares: Further Empirical Evidence | |
| dc.type | 04A - Beitrag Sammelband | |
| dspace.entity.type | Publication | |
| fhnw.InventedHere | Yes | |
| fhnw.IsStudentsWork | no | |
| fhnw.PublishedSwitzerland | No | |
| fhnw.ReviewType | Anonymous ex ante peer review of a complete publication | |
| fhnw.affiliation.hochschule | Hochschule für Wirtschaft FHNW | de_CH |
| fhnw.affiliation.institut | Institute for Competitiveness and Communication | de_CH |
| fhnw.pagination | 135-167 | |
| fhnw.publicationState | Published | |
| fhnw.seriesNumber | 692 |