Dear all,
I have a question about two-sample problem. I am comparing coefficients
of two samples (poor and non-poor) and would like to investigate whether
the difference between two coefficients is statistically significant ('one
on one' level as well as 'overall' level). To compare coefficients from
the two datasets in OLS settings, I can just use a two-sample t-statistic
with a pooled variance estimate obtained from the models. I am wondering
whether this can be applied to multinomial logistic regressions.
Alternatively, someone might suggest interaction terms. But unfortunately,
it doesn't work for me because there are less power and sparsity issues
involved when I create as many interaction terms as predictors. And how
can I do a F test for overall model comparison in multinomial logistic
regressions?
It's hard to find out ways of comparing coefficients in multinomial
logistic regression settings. If you are aware of it, could you share it
with me? It would be greatly appreciated.
Thank you,
Ick-Joong Chung
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