I have a dataset with 57 independent variables, many of which are highly correlated with each other. I calculated the VIF numbers and plotted them against the standard errors of the estimated coefficients.
I know that the more the variable is correlated, the higher the standard error should be, but it does not look like that in the plot.
Does it mean that I can ignore multicollinearity?
I am not trying to predict anything through my model. I am only trying to get the coefficient estimates and explain them.