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In STB-53 (clogit, pp.22-23)it is said that conditional logit seeks to fit a model that
explains "why observation 1 had a positive outcome in
group 1 conditional on ONE of the observations in the
group having a positive outcome". However, in clogit
help, it is said that in the case of k(1i):k(2i)
matching, clogit uses a recursive algorithm to
compute the likelihood (so it can solve models which
are not restricted on only ONE positive outcome per
1) Am I right in saying that clogit (or xtlogit, fe) can estimate a model where there are SEVERAL ones and zeros for the same group (not just conditioning in
only ONE positive result in each group)?
2) It is only the predicted probability which is
conditional on there being only ONE outcome per group?
Thanks to all in advance!
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