Detection of Near-Nulticollinearity through Centered and Noncentered Regression
Metadatos
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MDPI
Materia
Centered model Noncentered model Intercept Essential multicollinearity Nonessential multicollinearity
Fecha
2020-06-07Referencia bibliográfica
Gómez, R. S., García, C. G., & Pérez, J. G. (2020). Detection of Near-Nulticollinearity through Centered and Noncentered Regression. Mathematics, 8(6). [doi: 10.3390/math8060931]
Patrocinador
University of AlmeriaResumen
This paper analyzes the diagnostic of near-multicollinearity in a multiple linear regression
from auxiliary centered (with intercept) and noncentered (without intercept) regressions. From these
auxiliary regressions, the centered and noncentered variance inflation factors (VIFs) are calculated.
An expression is also presented that relates both of them. In addition, this paper analyzes why the
VIF is not able to detect the relation between the intercept and the rest of the independent variables of
an econometric model. At the same time, an analysis is also provided to determine how the auxiliary
regression applied to calculate the VIF can be useful to detect this kind of multicollinearity.