What is the tolerable level for multicollinearity? and possible remedial measures for it? My data has issues of multicollinearity. Upon estimating, I found out that my data have high levels of VIF In however I was confused as to what level of VIF can I possibly ignore the issue of multicollinearity. 

As I search the web, some says that it should be less than 2.5, others 10 or 14.

My VIF results is this: 


varA: 18.74
varB: 11.14
varC: 10.03
varD: 5.99
varE: 2.86


What step should I probably do to address the issue? I have fixed sample size and I can't log transform my model since one of my variables ranges from a negative to positive number.
I cannot post this as a comment but researchers such as Farrar and Glauber (1976) have shown that if VIF ≥ 10, this indicates the presence of multicollinearity between explanatory variables. There is no black and white threshold for this within the literature, and it highly depends on your sources and on the person reviewing your work. Moreover, you have not made any mention of the estimator you are using, but as you may know, multicollinearity makes the OLS estimator is less precise. That being said, have you tried standardizing by subtracting the mean? More information on your model and or your own knowledge on this would be useful to get help, in my view.