<p>The only time you <em>must</em> exclude multicolinear variables is when one is a perfect linear combination of the other.</p>
<p>Excluding in other cases is more of a rule of thumb - if the Variance Inflation Factor (VIF) is greater than 5 you may want to exclude the problematic variable.  <span class="math-container">$VIF = \frac{1}{1-R^2}$</span>  where <span class="math-container">$R^2$</span> is from the regression of all other dependent variables on the potentially multicolinear dependent variable.</p>
<p>But again, there are cases where the importance of the variable outweighs this rule of thumb, and you ought to still include the potentially multicolinear variable.</p>

CC BY-SA 4.0
2021-10-03T09:08:47.753Z
+1, although even VIF>5 is not the only accepted rule of thumb, in some textbooks the recommendation is to only remove variables with VIF>10.
1muflon1
CC BY-SA 4.0
2021-10-19T07:56:39.613Z
@1muflon1, is there any reference textbook for the VIF>10 if it is convinient for you to retrieve then?
Nguyen Lis
CC BY-SA 4.0
2021-10-02T17:13:24.717Z
2021-10-02T17:13:24.717Z
RegressForward
<p>Normally in a research, we exclude one of two variables that they are correlated. I am wondering what is the universal benchmark (better if having reference) to exclude one of them. Conventional wisdom saying that it would be 0.7 or higher. Can I ask your opinion about that?</p>
<p>I am using a Difference-in-Differences estimator.</p>
<p>Many thanks and warm regards.</p>

<econometrics><nonexpert>
What is the universal benchmark for excluding a variable based on correlation?
Nguyen Lis
# What is the universal benchmark for excluding a variable based on correlation?

(Asked by: username_0 on 2021-10-02)

Normally in a research, we exclude one of two variables that they are correlated. I am wondering what is the universal benchmark (better if having reference) to exclude one of them. Conventional wisdom saying that it would be 0.7 or higher. Can I ask your opinion about that?

I am using a Difference-in-Differences estimator.

Many thanks and warm regards.

**Tags:** econometrics, nonexpert


---

## 1 answers

### Answer by username_1 on 2021-10-02. Score: 3 | Accepted answer

The only time you *must* exclude multicolinear variables is when one is a perfect linear combination of the other.

Excluding in other cases is more of a rule of thumb - if the Variance Inflation Factor (VIF) is greater than 5 you may want to exclude the problematic variable. $VIF = \frac{1}{1-R^2}$ where $R^2$ is from the regression of all other dependent variables on the potentially multicolinear dependent variable.

But again, there are cases where the importance of the variable outweighs this rule of thumb, and you ought to still include the potentially multicolinear variable.

> **Comments:**
> **username_2**: +1, although even VIF>5 is not the only accepted rule of thumb, in some textbooks the recommendation is to only remove variables with VIF>10.  
> **username_0**: @username_2, is there any reference textbook for the VIF>10 if it is convinient for you to retrieve then?  

---

