title: Test for Multicollinearity in Panel Data R
body: <p>I am running a panel data regression using the <code>plm</code> package in <code>R</code> and want to control for multicollinearity between the explanatory variables. <br>
  I know there is the <code>vif()</code> function in the <code>car</code>-package, however as far as I know, it cannot deal with panel data output. <br>
  The <code>plm</code> can do other diagnostics such as a unit root test but I found no method to calculate for multicollinearity.<br>
  <br>
  Is there a way to calculate a similar test to <code>vif</code>, or can I just regard each variable as a time-series, leaving out the panel information and run tests using the <code>car</code> package?
  <br>
  <br>
  I cannot disclose the data, but the problem should be relevant to all panel data models.<br> 
  The dimension is roughly 1,000 observations, over 50 time-periods. <br>
  The code I use looks like this:</p>
  
  <pre><code>pdata &lt;- plm.data(RegData, index=c("id","time"))
  fixed &lt;- plm(Y~X, data=pdata, model="within")
  </code></pre>
  
  <p>and then <br></p>
  
  <pre><code>vif(fixed) 
  </code></pre>
  
  <p>returns an error.</p>
  
  <p><br>
  Thank you in advance.</p>