I have a short panel dataset that I want to analyze. It contains panel data of multiple individuals. Each individual is assigned to 1 of 4 treatment groups and I am interested in the effect of these treatments. Basically I want to run this regression:

Y = X + Z + Condition (X, Z vary over time; Condition is a dummy variable and time-constant)

I can't use the fixed effects "within" model, because the variable I'm interested in will drop out - since it is time-invariant.

  • reformulated: I think the proper way to analyze this dataset regarding my variable of interest (Condition) is random effects.

However, I am not sure - is there a better way?

  • 1
    $\begingroup$ Questions solely about how software works are off-topic here, but you may have a real statistical question buried here. You may want to edit your question to clarify the underlying statistical issue. You may find that when you understand the statistical concepts involved, the software-specific elements are self-evident or at least easy to get from the documentation. $\endgroup$ – gung - Reinstate Monica Jul 28 '16 at 20:36
  • $\begingroup$ @newbie Is treatment random? $\endgroup$ – Dimitriy V. Masterov Jul 30 '16 at 4:24
  • $\begingroup$ yes, individuals are assigned to treatment conditions randomly $\endgroup$ – newbie Aug 1 '16 at 13:37

I'm not familiar with plm, but I would assume that the between option runs your model on the group means such that you are only explaining between group variance in your outcome. That may be appropriate if that is the only variance of interest, but many people are interested in explaining both within and between outcome variance, and therefore use random effects models. You can always group mean center your within variables and add the group means of the predictors to your random effects model to get the best of both worlds!

  • $\begingroup$ Thanks a lot. is someone familiar with plm? $\endgroup$ – newbie Jul 28 '16 at 11:06

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