I am trying to model the selection of panel members from a bench of judges, where the number of choices is assumed to be fixed in advance. (In my application, it is common to pick five or seven judges from a bench of 12).

I am using (time-varying and case-specific) variables measured on the different judges to explain their selection.

I have seen other authors use conditional logit models to assess the effects of judge-specific variables.

However, the conditional logit model does not seem appropriate for cases where multiple choices must be made without replacement.

In simulations that I have carried out, I can recover known coefficients using the mclogit package in R when only one choice is made.

However, when the top two choices are chosen, the estimated coefficients are much, much smaller than their known values.

I therefore wanted to know:

  • Am I right to think that the conditional logit model is inappropriate in situations of this type?
  • What would be an appropriate model for the situation I have described?
  • How (ideally) might it be estimated in R?

1 Answer 1


I guess you could run separate logits for each of the seven choices, estimating the probability that judge j will be included in the set of chosen judges.

But the clogit assumes that a single choice is made from a discrete, finite set of available choices. So you would have to think of the set of all possible combinations of 7 judges chosen from 12, which is probably a much too large set to work with practically.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.