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  • Multicollinearity Test for Panel Data

    Hi everyone.
    I am new to Stata and currently using it for my research.

    I am trying to run a multicollinearity test for my panel data.
    Should I use to VIF command or the estat vce, corr, or correl command? What are the differences?

    Thank you for your kind help.

  • #2
    There are no multicollinearity tests neither for panel, nor for any other data. The things you mention are diagnostics.

    You can use such diagnostics in panel data if you think they are useful. The random and the fixed effects models are simply OLS on transformed variables, so all the diagnostics should work just as in standard OLS.

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    • #3
      Hi, thank you for your response. I tried those 3 commands, and these are the results. What are the differences between these results? Thanks again.

      . vif

      Variable | VIF 1/VIF
      -------------+----------------------
      bm | 1.35 0.742879
      size | 1.34 0.744481
      roe | 1.24 0.805383
      beta | 1.04 0.957648
      turnover | 1.03 0.972894
      -------------+----------------------
      Mean VIF | 1.20

      . estat vce, corr

      Correlation matrix of coefficients of regress model

      e(V) | turnover beta roe bm size _cons
      -------------+------------------------------------------------------------
      turnover | 1.0000
      beta | -0.1231 1.0000
      roe | 0.0397 0.0906 1.0000
      bm | 0.0650 -0.0152 0.2741 1.0000
      size | 0.1022 -0.1617 -0.2194 0.3554 1.0000
      _cons | -0.1478 -0.0636 0.0637 -0.3382 -0.9549 1.0000

      . correl return turnover beta roe bm size
      (obs=462)

      | return turnover beta roe bm size
      -------------+------------------------------------------------------
      return | 1.0000
      turnover | 0.1016 1.0000
      beta | 0.1611 0.1129 1.0000
      roe | 0.1451 -0.0617 -0.0495 1.0000
      bm | -0.1971 -0.0096 -0.0267 -0.3832 1.0000
      size | 0.0685 -0.0830 0.1316 0.3458 -0.4396 1.0000


      .

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      • #4
        Like I told you the multicollinearity comes with the model that you are estimating.

        It does not make sense to check whether the levels of variables are multicollinear, if you are going to apply first difference on the variables, or the within transformation, or quasi-demeaning (random effects model).

        If you want to check for multicollinearity, choose what panel data model you want to use, and then check in this model.

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        • #5
          Hi, if I'm using Fixed Effect Model, does that mean I need to run xtreg .., fe first and then run the estat vce, corr command?

          Thanks!

          Comment


          • #6
            Nathalie:
            yes, that's the sequence.
            That said, when non-perfect (and then Stata chimes in by default omitting one of the culprits), multicollinearity is kinda of an oversold issue (see if interested chapter 23 of https://www.hup.harvard.edu/catalog....=9780674175440).
            Kind regards,
            Carlo
            (StataNow 18.5)

            Comment


            • #7
              Originally posted by Nathalie Ruth View Post
              Hi, if I'm using Fixed Effect Model, does that mean I need to run xtreg .., fe first and then run the estat vce, corr command?

              Thanks!
              Yes, if any of the diagnostics you mentioned are available post -xtreg- apply then post xtreg.

              If they are not available post xtreg, you can manually apply the within transformation -- there is a command for that -xtdata-.-- and then apply the standard diagnostics on the transformed variables.

              Comment

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