MTest: a bootstrap test for multicollinearity

Víctor Morales-Oñate and Bolívar Morales-Oñate

MPRA Paper from University Library of Munich, Germany

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.

Keywords: MTest; Multicollinearity; Non Parametric Statistics; Simulation (search for similar items in EconPapers)
JEL-codes: C1 C13 C15 (search for similar items in EconPapers)
Date: 2021-12-21
New Economics Papers: this item is included in nep-ore
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