All,
I'm hoping someone might know why I'm getting vastly different results from
two approaches to the same estimation problem.
My cases are cities, and my d.v. is the proportion of homes within each
city that has access to certain public services. Straight OLS is not
appropriate for a proportional d.v., but I can transform the d.v. to the
logistic, ln(p/(1-p)) and use OLS with weights (Greene, 6.3).
Since I know the counts from which the proportions are calculated, I should
also be able to use glogit (or gprobit), where the d.v. is the # of homes
with the service and the weight variable (called the popvar in the manual)
is total homes per city. Greene (19.4.3) seems to suggest that this is a
comparable method to the logistic-OLS approach.
However, the two models return different results, not just in terms of
standard errors, which I might expect, but in terms of coefficient
estimates. The coeffs for several of the main i.v.s of interest change
size, sign, and significance in the two models. Does anyone know why this
would be? Is one or the other model inappropriate for the estimation
problem I've described?
Thanks,
Matt Cleary
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