Detection of Near-Nulticollinearity through Centered and Noncentered Regression

Gómez, Rafael Briones; García, Celia Prados; Pérez, José Garcı́a

doi:10.3390/math8060931

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…This means that the intercept is excluded. For more details and methods on detecting essential and non-essential multicollinearity, see Salmerón-Gómez et al (2020).…”

Section: Vifmentioning

confidence: 99%

MTest: a bootstrap test for multicollinearity

Morales-Oñate

2023

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A nonparametric test based on bootstrap for detecting multicollinearity is proposed: MTest. This test gives statistical support to two of the most famous methods for detecting multicollinearity in applied work: Klein’s rule and Variance Inflation Factor (VIF for essential multicollinearity). As part of the procedure, MTest generates a bootstrap distribution for the coefficient of determination which: i) lets the researcher assess multicollinearity by setting a statistical significance "alfa", or more precisely, an achieved significance level (ASL) for a given threshold, ii) using a pairwise Kolmogorov-Smirnov (KS) test, establishes a guide for an educated removal of variables that are causing multicollinearity. In order to show the benefits of MTest, the procedure is computationally implemented in a function for linear regression models. This function is tested in numerical experiments that match the expected results. Finally, this paper makes an application of MTest to real data known to have multicollinearity problems and successfully detects multicollinearity with a given ASL.

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“…This means that the intercept is excluded. For more details and methods on detecting essential and non-essential multicollinearity, see Salmerón-Gómez et al (2020).…”

Section: Vifmentioning

confidence: 99%

MTest: a bootstrap test for multicollinearity

Morales-Oñate

2023

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“…Note that, to the best of our knowledge, the Stewart index is not calculated in any other package in R. It only can be obtained manipulating the vif command of the rms [6] package. This manipulation consists in introducing the intercept as an independent variable within the matrix X and indicating that the model does not have an intercept with the lm command: As shown by [13], after this manipulation the model is considered to be non centered and, consequently, the vif command will be calculating the Stewart index instead of VIF. Thus, in this situation the researcher wrongly considers that is calculating the VIF when, in fact, the Stewart index was obtained.…”

Section: The Car and Rms Packagesmentioning

confidence: 99%

MultiColl package and other packages to detect multicollinearity in R

Gómez

García

2021

Preprint

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This work presents a guide for the use of some of the functions of the multiColl [16] package in R [19] for the detection of near-multicollinearity. The main contribution, in comparison to other existing packages in R or other econometric software, is the treatment of qualitative independent variables and the intercept in the simple/multiple linear regression model. The main goal of this paper is to show the advantages of the multiColl package in R, comparing its results with the results obtained by other existing packages in R for the treatment of multicollinearity.

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“…Xtwentys 4 2 7 . 4 4 As shown by (13), after this manipulation the model is considered to be non centered and, consequently, the vif command will be calculating the Stewart index instead of VIF. Thus, in this situation the researcher wrongly considers that is calculating the VIF when, in fact, the Stewart index was obtained.…”

Section: Other Packages In Rmentioning

confidence: 99%