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Open AccessArticle

The VIF and MSE in Raise Regression

1
Department of Quantitative Methods for Economics and Business, University of Granada, 18010 Granada, Spain
2
Department of Economic Theory and History, University of Granada, 18010 Granada, Spain
3
Department of Economy and Company, University of Almería, 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(4), 605; https://doi.org/10.3390/math8040605
Received: 1 April 2020 / Accepted: 13 April 2020 / Published: 16 April 2020
(This article belongs to the Special Issue Quantitative Methods for Economics and Finance)
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. View Full-Text
Keywords: detection; mean square error; multicollinearity; raise regression; variance inflation factor detection; mean square error; multicollinearity; raise regression; variance inflation factor
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Salmerón Gómez, R.; Rodríguez Sánchez, A.; García, C.G.; García Pérez, J. The VIF and MSE in Raise Regression. Mathematics 2020, 8, 605.

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