# Outliers and Influential observations in fixed effects regression

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.

• Run your fe regression as a lsdv regression, which gives you access to what is implemented in ols – Christoph Hanck May 1 '15 at 7:28
• "Like for half of my observations I get large residuals. So I do not want to simply remove them as the model will lose significance." The first point is not a reason to remove observations from an analysis. The second point is not a reason to keep observations in an analysis. – AdamO Feb 9 '18 at 17:46
• Try DFBetas omitting clusters instead of single observations to assess the influence of a cluster of abnormal values rather than the influence of a single abnormal value. – AdamO Feb 9 '18 at 17:50

• Far better is to use statistical principles when specifying and fitting the model and to not "play with the data". One of the principles is choosing a model that is unlikely to be ruined by outliers so you don't have to worry about this so much, i.e., semiparametric regression models such as the proportional odds model for continuous $Y$. – Frank Harrell Jun 2 '18 at 11:57