The VIF and MSE in Raise Regression Salmerón Gómez, Román Rodríguez Sánchez, Ainara García García, Catalina Detection Mean square error Multicollinearity Raise regression Variance inflation factor We thank the anonymous referees for their useful suggestions. 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. 2020-06-08T12:20:42Z 2020-06-08T12:20:42Z 2020-04 info:eu-repo/semantics/article Salmerón Gómez, R.; Rodríguez Sánchez, A.; García, C.G.; García Pérez, J. The VIF and MSE in Raise Regression. Mathematics 2020, 8, 605. [doi:10.3390/math8040605] http://hdl.handle.net/10481/62392 doi:10.3390/math8040605 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España MDPI