The Variance Inflation Factor (VIF) is used to detect the severity of multicollinearity in ordinary least square (OLS) regression analysis. In EViews, you can calculate the VIF by using the following formula:
Where $R_i^2$ represents the unadjusted coefficient of determination for regressing the ith independent variable on the remaining independent variables. A VIF above 4 or tolerance below 0.25 indicates potential multicollinearity, while a VIF greater than 10 or tolerance below 0.1 suggests significant multicollinearity that needs to be addressed
.To use VIF to detect multicollinearity in EViews, you can follow these steps:
- Open your dataset in EViews.
- Click on “Quick” > “Group Statistics” > “Correlations.”
- Select the independent variables you want to check for multicollinearity.
- Calculate the VIF for each variable.
High VIF values indicate a higher degree of multicollinearity, which can affect the accuracy and reliability of the regression models
