期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 -, 期 -, 页码 -出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2022.2110594
关键词
Detection; Estimation; Mean squared error; Multicollinearity; Ridge regression; Variance inflation factor
资金
- University of Granada (Spain) [PP2019-EI-02]
- Andalusian Government's Counseling of Economic Transformation, Industry, Knowledge and Universities (Spain) [A-SEJ496-UGR20]
- project I+D+i - MCIN/AEI [PID2019-107767GA-I0]
This paper proposes using the VIF criteria to select the biased ridge parameter and provides evidence of its effectiveness through Monte Carlo simulation and real-life applications.
Ridge Estimation (RE) is a widespread method to overcome the problem of collinearity defining a class of estimators depending on the non-negative scalar parameter k. A great number of papers focus on the estimation of this biasing parameter. Traditionally, the mean squared error criterion is used to compare the performance of the different proposed estimators. However, the minimization of the mean squared error (MSE) does not always guarantee the mitigation of collinearity, meaning it is possible, for example, to obtain a variance inflation factor (VIF) higher than 10 for the k that minimizes the MSE. In this paper, we propose the VIF criteria to select the biased ridge parameter. A Monte Carlo simulation is presented with results that support this idea. Also, two real life empirical applications are used to illustrate the contribution of this paper.
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