I am running a fixed effects regression with a very unbalanced panel data. There are a lot of large residuals. For half of my observations, the residuals are large. However, I do not want to simply remove them as the model is not statistically significant when omitting these observations.
If I just rely on added variable plots to look at only extreme outliers, I can't exactly tell which observation in the cluster is extreme, unlike in cross sectional data.
So I was thinking if I also check influence by something like Cook's distance.
But how do we identify influential observations in fixed effects regression. Is there a command like Cook's distance as in ordinary least squares.
Best Answer
Yes, you should be able to look at measures like Cook's Distance or Leverage. There is usually a command to obtain these in whatever statistical software you are using (you don't mention what you are using).