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Re: st: selection correction in survival models

From   "Austin Nichols" <[email protected]>
To   [email protected]
Subject   Re: st: selection correction in survival models
Date   Thu, 11 Sep 2008 09:59:58 -0400

Murali Kuchibhotla <[email protected]>:
A better question might be: Is there a way to correct for this in the
real world?  Probably not, in most senses.  Think of it as a
logit/probit regressing "survival" on the k variables in X and a
training dummy T and the k interactions T*X, so you have at least k+1
endogenous variables measuring the effects of training.  You could
switch to an -ivprobit- model if you had a large number of
instruments, but that seems unlikely (plus, I think you would want an
-ivcloglog- command which does not exist).  One way forward is to
reweight the samples so they look identical on observables, using
propensity scores, as described in and elsewhere
(the sample will look identical in the sense that a -hotelling- test
would fail to reject the null that the mean of X is the same in the
training group and the non-training group--the distributions may
differ in other ways).  But this corrects only for selection on
observables, not unobservable differences--for the latter, you need
training to be randomly assigned, for a start.  Or you need a very
convincing natural experiment.

On Wed, Sep 10, 2008 at 11:22 PM, Murali Kuchibhotla
<[email protected]> wrote:
> Hello,
>      I am interested in estimating separate Cox regression models for 2
> groups of individuals- trainees and non-trainees. The problem is that the
> training decision is endogenously determined, so that the differences
> between the 2 sets of parameter estimates may well be driven by the
> differences in the unobservable characteristics between the 2 groups of
> respondents. Is there a way to correct for this in Stata?
> Murali Kuchibhotla
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