What is the difference between xtreg and reg in stata

Industry- would give you the same results as -xtreg, fe-. But -robust- means different things to -reg- and -xtreg, fe-. In -reg-, -robust- gives you an unclustered standard error that is nevertheless robust to heteroscedasticity. In -xtreg, fe-, the unclustered standard error is invalid, so Stata automatically converts it to a clustered standard error, clustering on your panel variable.

Since the unclustered standard error is not valid for this kind of analysis, the -reg- results should not be used. Comment Post Cancel.

Dear Clyde, Thank you very much for your fast and detailed response. In fact, I learned from one of your posts that I should include the i. Intuitively this makes sense to me but I do not have a full explanation for it. Could you motivate one if possible? Thanks again! Best, Leon. Well, perhaps you learned the wrong lesson from something I wrote. I do not advocate always adding i. I advocate it when the outcome variable is one that is subject to yearly shocks that ought to be taken into account.

If the outcome variable is stable, then i. The coefficient estimates and standard errors are the same. The calculation of the R 2 is different. In the areg procedure, you are estimating coefficients for each of your covariates plus each dummy variable for your groups. In the xtreg, fe procedure the R 2 reported is obtained by only fitting a mean deviated model where the effects of the groups all of the dummy variables are assumed to be fixed quantities.

So, all of the effects for the groups are simply subtracted out of the model, and no attempt is made to quantify their overall effect on the fit of the model. Regardless of which approach you take, the SSE sum-of-squares error is the same. In the areg approach, the SST sum-of-squares total is given by. In the xtreg, fe approach, the R 2 reported is not the R 2 that is calculated from the regression for areg but the regression for the mean detrended dataset.

In our example, because the within- and between-effects are orthogonal, thus the re produces the same results as the individual fe and be. With more general panel datasets the results of the fe and be won't necessarily add up in the same manner. Click here to report an error on this page or leave a comment.

Your Name required. Your Email must be a valid email for us to receive the report! How to cite this page. Notice that there are now estimates for both a and b.