Hello, everyone,
I have a few basic questions about using glm to deal with the
retransformation problem.
I am estimating a model where y is vitamin A consumption, and among
the x's is participation in a nutritional intervention. The vitamin A
consumption variable was highly skewed, so I ran
svyreg lny x. (The distribution of lny is sufficiently close to normal.)
Unfortunately, the results were a little too high for me to really
believe, so I also ran
svyreg y x
which yielded something a bit more reasonable.
It was suggested that I might be seeing the retransformation problem
at work. The homoskedasticity of the error terms from svyreg lny x is
rejected.
I started to work with glm and a log link, but I have a few basic
(sorry) questions.
First, in the glm estimation should y be the dependent variable or
lny? That is, do I want to write " glm y x, link(log)" or "glm lny x,
link(log)." I think it's the first, but I'm not positive.
Second, I've been using the default Gaussian for the family. Is there
a reason to use a different distribution like gamma or Poisson?
Third, for simplicity, say x is a dummy variable. After I run "glm y
x, link(log)" I ask Stata to exponentiate with eform. Are the results
it gives after eform
Exp(xB) where x=1
-----------------------------
Exp(xB) where x=0
evaluated at the mean? If not, what are they?
Thanks in advance for your help!
Krista Jacobs
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