Thanks to Jay for mentioning -betafit- (from SSC).
-betafit- does not purport to support panel structure. Off-hand, I can't
say how much of a problem that might be.
If this were my problem, I'd be looking at -xtgee-.
Nick
[email protected]
Verkuilen, Jay
Nick Cox outlined several strategies, to which I will add just a bit:
(1) How close to the boundaries are your observations? If the
distribution looks reasonably symmetric, you probably won't gain much
from using a specialized model that "knows" about the boundaries. If you
have some skew due to the ceiling or floor but not a truly L- or
J-shaped distribution, the logit transformation will probably normalize
your errors enough to do ordinary panel regression models. If the
distribution is truly L- or J-shaped, no transformation will fix things
up.
(2) -betafit- (by Nick, Maarten, et al) with clustered robust SEs is a
viable alternative that uses the linking strategy. This uses the beta
distribution as an error model. It won't adjust for the autoregressive
nature of panel data, though, but maybe that'll work well enough.
(3) If you want to use the GLM approach and are willing to move to
different software (SAS or winBUGS), I can give you examples to do
random effects and AR-adjusted beta regression. (One of these days, I
want to port a random effects and GEE betareg over to Stata; no time
right now.)
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