The VIF and MSE in Raise Regression
| Autor: | Catalina Beatriz García García, Román Salmerón Gómez, Ainara Rodríguez Sánchez, José García Pérez |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mean squared error
General Mathematics detection 01 natural sciences mean square error 010104 statistics & probability Lasso (statistics) Bias of an estimator 0502 economics and business Computer Science (miscellaneous) Econometrics multicollinearity 0101 mathematics raise regression Engineering (miscellaneous) 050205 econometrics Mathematics Variance inflation factor lcsh:Mathematics 05 social sciences Estimator Collinearity lcsh:QA1-939 Multicollinearity Ordinary least squares variance inflation factor |
| Zdroj: | riUAL. Repositorio Institucional de la Universidad de Almería Universidad de Almería Digibug. Repositorio Institucional de la Universidad de Granada instname Mathematics Volume 8 Issue 4 Mathematics, Vol 8, Iss 605, p 605 (2020) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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