Hi Ian, Jean, Anders, and Maarten,
Thank you everyone for responding to my query.
I am quite sure that the formula suggested by Ian wouldn't
work because, as indicated by Maarten, it would have to be
something that reflects the bivariate logistic
distribution. I am aware of the bivariate normal formula
mentioned below but it wouldn't apply for a logit model.
I was only wondering whether there is something similar for
a logit model because I had found this link in the
archives, which shows the IMR calculation for -mlogit- (but
not for -logit- explicitly.) Check this:
http://www.stata.com/statalist/archive/2003-04/msg00465.html
As for Jean's suggestion about -heckprob-: I didn't want to
use that because 1) I have already tried it and it's taking
forever to converge, and 2) -logit- fits my data better
than -probit- and so, I wanted to use something like
"heckman + logit".
Finally, Anders' suggestion: it is not immediately clear to
me how -gllamm- is applicable in my case but I will check
again.
In any case, thanks a lot for the discussion.
If anyone can check the above link and add something for
-logit-, that'd be great.
Thanks,
Nishant
--- Ian Watson <[email protected]> wrote:
> Nishant
>
> The approach you outline will work. The steps in Stata
> are straightforward.
>
> For example, if participation has two outcomes (working
> or not working),
> and you want an inverse mills ratio for the working
> outcome:
>
> logit work age edu children region
> capture drop phat
> capture drop imr
> predict phat if e(sample), xb
> gen imr = normden(phat)/norm(phat)
>
>
> (Note that the capture drop lines are only needed if you
> insert this
> code into a do file which runs multiple times).
>
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