Re: st: Problem with margins after logit on a person period data

[Urmi Bhattacharya <ub3@indiana.edu>]

Hi Austin and fellow statalisters,

I am terribly sorry if I am reposting it again. I got a bounce back
notice and hence I am trying to post it again.


In reply to the issues you mentioned:


I wonder if I could circumvent this problem you mentioned by
considering the clogclog regression instaed of logit because then what
I am modeling is the discrete hazard of dropping out in an interval
conditional upon having survived till before that.


So I could do the following

clogclog school_left childage i.childfemale i.urban i.scstobc
i.casteother i.dadp i.dadm i.momp i.momm wagep wage5 wage8 wage9 distp
distm disth percapcons durat1 durat2 durat3 durat4 durat5 durat6
durat7 durat8 durat9 durat10 durat11, nocons nolog

My goal is to find investigate the if the effect of mothers
education(momp) on the probability of dropping out varies with
duration.

So if I find the predicted probability when momp=1 and durat1=1 and
the predicted probability when momp=1 and durat2=1, then look at the
estimated change in probability and see that the estimated change is
significantly positive, then could I use this as evidence to say that
in risk period 2, momp=1 matters more than in risk period1 in terms of
interval hazard of dropping out?

Best

Urmi
On Thu, Jun 9, 2011 at 12:17 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> Urmi Bhattacharya <ub3@indiana.edu> :
>
> This whole exercise is highly suspect--you are computing marginal
> effects over a sample of periods at risk, not people.  Note that
> people are in your model for very different numbers of periods, but
> you are averaging over all periods; what is the goal here?  You said
> you are "interested in the marginal effects of the variables on the
> probability of hazard" which I think means you want to measure the
> marginal effects of the variables on the conditional probability of
> leaving school (conditional on not having left yet) at different
> durations, which means you should calculate marginal effects for each
> sample of people still at risk, at different durations.  These are
> unlikely to be very informative in the probability metric, however;
> odds ratios are used for a reason for such applications.
>
> On Wed, Jun 8, 2011 at 10:31 PM, Urmi Bhattacharya <ub3@indiana.edu> wrote:
>> Hi,
>>
>> I dropped one of the duration dummies durat1 and ran the following
>> logit regression
> ....
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