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VIF stands for Variance Inflation Factor. Values lower than 4 show that the model is okay. High VIF-scores pinpoint to multicollinearity.

What is the use of VIF

In multiple regression several variables are used. If two or more variables have a strong mutual relationship, they have great corresponding predictive power. Than one or more of these variables can then be omitted.

Most of the time the correlation coefficient between predictive variables is a good indicator. To identify these combinations the Variable Inflation Factor (VIF) is computed. This makes it easier to identify multicollinearity due to a combination of variables.

If only one predictive variable is used, the VIF value is 1. If variables are added, the value increases. Small increases indicate that the model is okay. However, how high should the VIF value become to indicate the model shows multicollinearity?

How to interpret the VIF

Most authors in textbooks write that VIF values lower than 4 show that the model is okay. Some authors draw the line for the VIF values at 2. I think this value is too low. But you also have authors who retain variables in the model with VIF values up to 10. Best of all, the model is good for VIF values up to 4. Just follow the majority.

Related topics to VIF

• Correlation 
• Multiple correlation 
• Regression 
• Multiple regression

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