Hello,
I am pretty new to mlogit, and still trying to figure out what models to use.I
have a data set of N individuals, each of which faces I alternatives. The
utility function of individual n, for choice i is:
u(i,n) = alpha(i) * x1(i,n) + beta * x2(i,n)
where alpha(i) is the individual specific parameter, and beta is shared among
all individuals. I don't really know how to set this up in mlogit.
If I assumed that beta is individual-specific (beta(i)), then I can divide the
data set to many subsets, each of which corresponds to a particular individual
i, and run this model for each subset to estimate alpha(i) and beta(i).
y ~ x1 + x2
This can be done just fine.
I have gone over tutorials by Train and by Heshner but I haven't found out how
to solve this problem yet. Any suggestions are welcome. Thank you so much for
your time!
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