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st: Correcting Montone Likelihood in Cox Regressions


From   <J.M.Chwieroth@lse.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Correcting Montone Likelihood in Cox Regressions
Date   Wed, 20 Feb 2008 12:13:20 -0000

Dear Stata Community:

I am trying to estimate a model that explains the decision to appoint a
particular type of government official (where 1=appointment of that type
of official; 0=otherwise).   Using somewhat similar data in the past, I
was able to use a cox proportional hazard model because I had a large
number of observations (over 600) and a fairly large number of
"failures" (around 100).   I used this type of model rather than a
logit/probit because of its flexibility and the ability to include fixed
effects to capture heterogeneity in the data without "loosing" a bunch
of observations.

But for the current paper I have just 350 observations and only around
20 "failures" (6% or so of the data).  Without a sufficiently large
number of observations and "failures," the model produces massive (i.e.
some E+24) or miniscule (i.e. some E-14) coefficients and standard
errors.  Bootstrapping also does not help.  Because of this I am
considering abandoning the event history approach and using a rare
events logit instead, but I would prefer to remain with the former.  

It has been suggested to me that I am encountering the problem of
monotone likelihood, that is, that the likelihood seems to converge but
it produces parameter estimates that diverge to plus or minus infinity.

If this is the case, then the issue I am facing, and one on which I hope
the STATA community can offer assistance, is that there appears to not
yet exist any software code for STATA users to implement the correction
that has been advocated for such a problem.  George Heinze has written a
code for R and SAS users but has not yet written one for STATA users,
though I have requested that he do so.   

Thus, I was hoping that the STATA community are aware of (or perhaps
have written or could write) such a code for STATA users;  or,
alternatively, could recommend another approach to this problem.

Cordially,
Jeff Chwieroth

Jeffrey Chwieroth
Lecturer in International Political Economy
Department of International Relations
London School of Economics
Houghton Street
London WC2A 2AE
United Kingdom
Tel: +44 (0) 20 7955 7209
Fax: +44 (0) 20 7955 7446
Website: http://personal.lse.ac.uk/chwierot/


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