Implausibly small standard error I have data of operation success of many doctors. I estimated a regression using Stata with fix effects on the individual doctors.
I first ran the regression using robust option. The resulted t value of estimates of individual doctors ranges from 2.17 to 6.14.  Then I re-ran it using the vce(cluster doctor) option. I expected the standard errors would become large. However, I indeed got smaller std. error -- much smaller, for example, 1.04e-14. It's just too good to be true. Why is that? Any possible reason? 
 A: You have way overcorrected the individual doctor effects twice using methods that simply do not work together.
If your model is regress outcome i.doctor, vce(cluster doctor), then Stata should have complained that you've exhausted your degrees of freedom. xtreg may not be as smart, and may miss a perfect determination of the fixed effects. These 1e-14 standard errors should have been identically zero, and they are non-zero in practice due to rounding somewhere in the guts of fixed effect estimation. What happens here is this:


*

*cluster variance estimation works by summing up the cluster contributions, over clusters. However,

*by specifying doctors as fixed effects, you force the residuals for a given doctor to sum up to 0.

*regress knows how to determine this at the level of algebra. xtreg may not know enough of computational linear algebra to do this, though, and simply sums up the (numerical) zero contributions to produce the implausibly small standard errors that you see here.

