If we estimate using OLS then we have a biased coefficient on our variable of interest. But we can't measure "management skills". This is the whole benefit of using FE! Suppose we think that some firms have better managers and that explains to some degree why the outcome variable is higher for such firms (say the outcome variable is profit and firms which are better managed are more profitable). If you have a panel dataset then you are probably better off using clustered standard errors as your heteroskedasticity will be related to the reporting of each unit (firms).Ī regression estimated using FE will differ from OLS (I assume that is the alternative you talk about) because the FE removes time-invariant characteristics.
This will adjust the standard errors to take account of the heteroskedasticity.
In a pooled dataset with heteroskedasticity you should use robust standard errors. they are the same across time for each unit)? If not, please share your Stata commands and some info on the dataset so we can see what is going on. Therefore, presumably the variables that are being dropped are time-invariant (i.e.
#Cluster standard errors stata manual
I suggest you do some searches or look in a textbook for the basic econometric procedure of a fixed effects estimator (the Stata manual for xtreg will also be useful). Fixed effects will remove time-invariant characteristics.
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