An improved robust variance inflation factor: Reducing the negative effects of good leverage points

Abstract

In multiple linear regression analysis, the variance inflation factor is a well-known collinearity measure. It is defined as the function of the coefficient of determination between the explanatory variables, and it is based on the maximum likelihood estimator of the regression coefficients. Nevertheless, in addition to outliers, leverage observations can have significant impact on the coefficient of determination, and thereby the variance inflation factor. This study presents an improved robust variance inflation factor estimator that is not affected by these observations. Simulation studies and a real data analysis indicate that the modified robust variance inflation factor estimator performs better than the traditional one.

The impact of sales growth on manufacturing companies' profitability in the Republic of Serbia: Panel data analysis

“…"Before starting a regression analysis, it is important to investigate whether there are outliers and/or collinearity problems in the data" (Ekiz, 2023). The variance inflation factor (VIF) is one of the most powerful tests of multicollinearity.…”