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MTest: a bootstrap test for multicollinearity

Morales-Oñate, Víctor and Morales-Oñate, Bolívar (2021): MTest: a bootstrap test for multicollinearity.

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Abstract

A non parametric 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). Mtest lets the researcher set a statistical significance, or more precisely, an achieved significance level (ASL). In order to show the benefits of MTest, the procedure is computationally implemented in a function for linear regression models. These 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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