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

[Austin Nichols <austinnichols@gmail.com>]

Urmi Bhattacharya <ub3@indiana.edu> :

I don't see how using -cloglog- instead of -logit- changes anything.
Either way you are modeling the hazard.

On Thu, Jun 9, 2011 at 1:36 PM, Urmi Bhattacharya <ub3@indiana.edu> wrote:
> Hi Austin,
>
> 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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