Hello Stas,
Thank you for your reply. I will have to spend some time reviewing the
options you've suggested. Some of the information you've provided is
somewhat advanced (read: over my head), but I will read up on what you've
suggested.
Thanks again,
Margaret
> Margaret,
>
> my understanding is that by unobserved heterogeneity econometric slang
> you just mean non-zero variance of the random effects. (The list FAQ
> strongly suggests using the terms that most people on the list would
> understand, not only the specialists in your narrow field.)
>
> -svy- poorly interacts with -xt-, and it is for a reason. -svy-
> commands work with what is called design-based inference paradigm:
> there is a finite population of interest from which a random sample is
> taken -- most likely, with complex design including stratification,
> clustering, and differential probabilities of selection. When you go
> collecting the data again in the longitudinal manner, the question
> would arise, what is the population to which the results can be
> generalized? Is it the original population of say 1990, the first year
> in which the data were collected? Is it the population that has been
> present during the period 1990-2000? Is it the population in the end
> of the period? Is it each of the populations between 1990 and 2000?
> Technically, if your sample was taken in 1990, then it is the only
> population to which the results would generalize, and the (original
> 1990) weights would then work to provide unbiased (or rather
> consistent, for nonlinear models) estimates of the corresponding
> population quantities.
>
> Now, the mechanics of the linearized vce is that it needs the
> likelihood scores for each unit (i.e., panel), and it needs the
> weights to be constant within that panel. In fact, the whole
> pseudo-likelihood procedure is based on computing the integrals over
> the random effects for the -re- models, or conditioning on the number
> of successes in the -fe- models, and what enters the likelihood is a
> certain function of all observations within the panel. That's what the
> weight can be attached to, and from that it is clear that the weights
> cannot vary within panels, and at least that is tricky, then.
>
> Further about the mechanics: the linearized vce estimator needs the
> scores to be computable from the likelihood or pseudo-likelihood (and
> feeding them to -_robust- calculator of the sandwich variances... if
> you understand the mechanics of Eicker-White heteroskedasticity
> consistent estimator, then you know more than half of that mechanics.)
> -xtlogit- does not -predict- the scores; must be for a reason, too --
> it would again be at the panel level, and it would look like an
> integral over the random effects. Frankly, I don't know whether those
> scores are at all computable for this particular model.
>
> Well, now about the conceptual approach: I said that the -svy-
> commands assume the fixed characteristics, yet -xt*, re- deals with
> something randomly distributed in the population, rather than fixed.
> That is a conceptual contradiction to which I don't have a good
> answer. Whoever manages to push this through might be able to open a
> whole new area in survey statistics. (Yes, I am aware of model-based
> and model-assisted estimation, but it is a different story.)
>
> So this standard route with -svy: xtlogit- seems to be closed. Is that
> the end of the world? Well... there's a couple of alternatives.
>
> First, you can reformulate your model to be estimated by -gllamm- that
> allows for multilevel weights. Mastering -gllamm- is a formidable
> task, but pays off very well in the end. I won't even start to give
> indications as to how to proceed with it -- you would have to take a
> couple of weeks to figure it, as it is a whole estimation paradigm in
> itself, and you really need to understand it deeply to make it work.
>
> Second, you can specify -svy, jackknife : xtlogit - instead, and see
> what comes out. If you have 5000 panels, the -svy jackknife- would
> need to re-estimate the model 5000 times, so be prepared to have your
> computing unit chew on it for a couple of days. Even at that, I would
> tend to think that the variance of the random effect would be treated
> as an ancillary parameter, and so you might have to take some special
> action to make the jackknife report that variance, to put a confidence
> interval on it.
>
> Here's yet another subtlety, if not a caveat: I tend to think that
> both -xtlogit- and -gllamm- would work by freely estimating the
> variance of random effects, and effectively fixing the variance of the
> individual random effects to _pi^2/6. It might be argued that instead
> the total variance of u_i+e_{ij} should be fixed, to make the results
> comparable with those of -logit-. This would be especially relevant in
> the resampling estimators like -svy jackknife-, as it is not quite
> clear whether the results of those 5000 re-estimates are actually
> comparable to one another. In jackknife, you are dropping panels one
> by one; of course this would be changing the estimates of the
> variance, but if you fix that variance, it would instead affect the
> point estimates, as only the ratio of the logit coefficients to the
> total error standard deviation is identified. So I would not be
> terribly convinced by those results, either.
>
> Ohio State has the Center for Survey Research
> (http://www.csr.ohio-state.edu/), so I imagine you can talk to
> somebody there. They seem to be more like the consulting center to me
> though, so I don't know if they are able to provide strong advice on a
> complex model such as -xtlogit-
>
> Hope this helps.
>
> On 2/24/07, Margaret Gassanov <gassanov.1@osu.edu> wrote:
>> Hello,
>>
>> I am trying to run a discrete-time model (random-effects), correcting
>> for
>> unobserved heterogeneity, and using weights for complex survey data.
>>
>> Unfortunately, I am having a problem getting this model to run.
>>
>> This is my syntax:
>> svyset [pweight=weight], strata(region) psu(psu)
>> svy, subpop(sampled): xtlogit DV [list of IVs], i(id)
>>
>> I get this response: xtlogit is not supported by svy with
>> vce(linearized);
>> see help survey for a list of Stata estimation commands that are
>> supported
>> by svy
>>
>> If I run the model without the survey command, I see that I do have
>> unobserved heterogeneity. I can also run the model with the weights but
>> not with the checks for unobserved heterogeneity. But I cannot seem to
>> do
>> both.
>>
>> Is there another method I could do to get around this? Or is there
>> another way to do event-history analysis that uses both survey weights
>> and
>> corrects for unobserved heterogeneity? I would be very happy to hear
>> any
>> suggestions.
>>
>
>
> --
> Stas Kolenikov
> http://stas.kolenikov.name
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>
>
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