The VIF and MSE in Raise Regression

Román Salmerón Gómez, Ainara Rodríguez Sánchez, Catalina García García and José García Pérez
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Román Salmerón Gómez: Department of Quantitative Methods for Economics and Business, University of Granada, 18010 Granada, Spain
Ainara Rodríguez Sánchez: Department of Economic Theory and History, University of Granada, 18010 Granada, Spain
Catalina García García: Department of Quantitative Methods for Economics and Business, University of Granada, 18010 Granada, Spain
José García Pérez: Department of Economy and Company, University of Almería, 04120 Almería, Spain

Mathematics, 2020, vol. 8, issue 4, 1-28

Abstract: 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.

Keywords: detection; mean square error; multicollinearity; raise regression; variance inflation factor (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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