The VIF and MSE in Raise Regression
The raise regression has been proposed as an alternative to ordinary least squares estimation when a model presents collinearity. In order to analyze whether the problem has been mitigated, it is necessary to develop measures to detect collinearity after the application of the raise regression. This...
| Main Authors: | , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/8101 |
| _version_ | 1789406501286182912 |
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| author | Salmerón Gómez, Román Rodríguez Sánchez, Ainara García García, Catalina García Pérez, José |
| author_facet | Salmerón Gómez, Román Rodríguez Sánchez, Ainara García García, Catalina García Pérez, José |
| author_sort | Salmerón Gómez, Román |
| collection | DSpace |
| description | The raise regression has been proposed as an alternative to ordinary least squares estimation when a model presents collinearity. In order to analyze whether the problem has been mitigated, it is necessary to develop measures to detect collinearity after the application of the raise regression. This paper extends the concept of the variance inflation factor to be applied in a raise regression. The relevance of this extension is that it can be applied to determine the raising factor which allows an optimal application of this technique. The mean square error is also calculated since the raise regression provides a biased estimator. The results are illustrated by two empirical examples where the application of the raise estimator is compared to the application of the ridge and Lasso estimators that are commonly applied to estimate models with multicollinearity as an alternative to ordinary least squares. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-8101 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-81012023-04-12T19:07:12Z The VIF and MSE in Raise Regression Salmerón Gómez, Román Rodríguez Sánchez, Ainara García García, Catalina García Pérez, José detection mean square error multicollinearity raise regression variance inflation factor The raise regression has been proposed as an alternative to ordinary least squares estimation when a model presents collinearity. In order to analyze whether the problem has been mitigated, it is necessary to develop measures to detect collinearity after the application of the raise regression. This paper extends the concept of the variance inflation factor to be applied in a raise regression. The relevance of this extension is that it can be applied to determine the raising factor which allows an optimal application of this technique. The mean square error is also calculated since the raise regression provides a biased estimator. The results are illustrated by two empirical examples where the application of the raise estimator is compared to the application of the ridge and Lasso estimators that are commonly applied to estimate models with multicollinearity as an alternative to ordinary least squares. 2020-04-28T08:07:23Z 2020-04-28T08:07:23Z 2020-04-16 info:eu-repo/semantics/article 2227-7390 http://hdl.handle.net/10835/8101 en https://www.mdpi.com/2227-7390/8/4/605 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | detection mean square error multicollinearity raise regression variance inflation factor Salmerón Gómez, Román Rodríguez Sánchez, Ainara García García, Catalina García Pérez, José The VIF and MSE in Raise Regression |
| title | The VIF and MSE in Raise Regression |
| title_full | The VIF and MSE in Raise Regression |
| title_fullStr | The VIF and MSE in Raise Regression |
| title_full_unstemmed | The VIF and MSE in Raise Regression |
| title_short | The VIF and MSE in Raise Regression |
| title_sort | vif and mse in raise regression |
| topic | detection mean square error multicollinearity raise regression variance inflation factor |
| url | http://hdl.handle.net/10835/8101 |
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