Can there be a good explanation for this:

I’m modeling discrete binary choice (0 or 1) based on choice attributes (i.e., attributes-only without introducing individual heterogeneity). Based on the same dataset (n=1000) I get (somewhat slightly) different coefficients using the following two packages:

(1) ￼mlogit, which relies on maximum likelihood

(2) glm (family = binomial(link = "logit") ￼which I believe (?) also relies on max likelihood.

Info from CRAN’s Econometric’s Task View classifies “Generalized linear models (GLMs)” different from “Multinomial Responses” , but in my case of binary choice shouldn’t they give same result? Is there some small difference that I’m unaware of? I’m not savvy enough to decipher all the code “under the hood” of these two R packages.

Or are the differences in coefficients — shown in screen shot below — so small that it doesn’t matter (worth noting that coefficient signs are the same and relative weight isn’t too far off)?

Thx

Scott

On related note i’m aware that multinom() is designed for three outcomes or more, but gives the same result as glm(family = binomial(link = "logit”), when both use a dep var with two outcomes. See here.