I am a little confused with the different robust variance options in Stata.
I've read the manuals (R: regress, p.352; R: xtgee, p.79; U, p.270) but I
am still uncertain on some points.
Suppose I have repeated measures on each unit ("id").
Contrast
(1) - regress ..., cluster(id) -
to
(2) - glm ..., cluster(id) -
(3) - xtreg ..., pa i(id) corr(indep) robust -
(4) - xtgee ..., i(id) corr(indep) robust -
(2), (3), and (4) all produce identical estimates and standard errors. But:
although those estimates are identical to those obtained from (1), the
standard errors are not.
Example with the AUTO dataset (with "manufacturer" as the unit of
clustering) follows at the end.
--- QUESTIONS ---
I would have expected all four commands to give identical results.
Is the difference the fact that the "cluster-robust" approach (1) takes
into account any (arbitrary) correlation structure within subject, while
the GEE approach (3&4) uses the "working correlation" (in this case,
independence), to fix up the variance? But in terms of calculations,
doesn't the "cluster-robust" approach amount to a GEE with independence
working correlation structure within cluster?
Even if that's not the case, however, why aren't the -glm- (2) results
identical to the -regress- (1) results?
Or is it simply that the "finite sample correction" [R: regress, p.252; U,
p.275] makes (1) different from all else?
By the way, the "nmp" option in -xtgee- does not seem to have an effect
when corr(indep), although it does have an impact when corr(exch).
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