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Re: st: mlogit iterations not concave


From   Partha Deb <partha.deb@hunter.cuny.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: mlogit iterations not concave
Date   Sun, 21 Mar 2010 16:46:08 -0400

Jessica,

I suggested you reduce the number of categories for your dependent variable. It should help you identify sources of singularity or near-singularity, which is almost certainly the reason for the "non concavity". I though you were including county-indicators but now it seems that you have county-level continuous variables. If so, keep them as continuous variables. Categorizing those makes is more likely you run into problems with "empty" cells. Your overall problem is, almost surely, that your model is not well specified.

To get a feel for where your problem is add the -iter(20) option to your -mlogit- command. -mlogit- will then stop iterating after 20 iterations and produce a table of coefficients / standard errors. Look for coefficients that are too big / small and/or standard errors that are too big / small or "." .

Hope this helps.

Partha


Jessica Bishop-Royse wrote:
Dear StataListers,
I am working on a project where I am trying to estimate a mlogit for a 9
category nominal dv (cause of death), using both county level predictors (67
counties) and individual level predictors (continuous vars like education
and age as well as dummied vars like not married). When I run county level
ivs by themselves with the dv, I get significant results.  When I run
individual level ivs by themselves with the dv I get signficant results.
When I try to incorporate both, county level predictors don't seem to be
significant.

I originally was trying to estimate multilevel models (gllamm) when it was
suggested that I try mlogit for the timebeing.  I also dummied the county
level variables (so that they are no longer continuious, but rather
representative of established cutpoints).  It was also suggested last week
that I collapse my dv into fewer categories.  However, when I do this, Stata
runs and runs and runs and gives me the message that iterations are "not
concave".  However, no such problems modeling with the 9 category dependent
variable (just not significant results for county level predictors).

My question is this:  what is this "not concave" message?  And is this an
indicator that I need to abandon mlogit? Opinion?

. mlogit cause6death black maternaled maternalage real_parity for_born
pncint pncadqp pncindq crisisbirth multiple if birth1980==1, rrr

Iteration 0:   log likelihood =  -10390.13
Iteration 1:   log likelihood = -10242.229
Iteration 2:   log likelihood = -9337.9062
Iteration 3:   log likelihood = -9238.5535
Iteration 4:   log likelihood = -9220.1816
Iteration 5:   log likelihood = -9217.2186
Iteration 6:   log likelihood = -9216.5682
Iteration 7:   log likelihood = -9216.4613
Iteration 8:   log likelihood = -9216.4493
Iteration 9:   log likelihood = -9216.4468
Iteration 10:  log likelihood = -9216.4463
Iteration 11:  log likelihood = -9216.4461
Iteration 12:  log likelihood = -9216.4461
Iteration 13:  log likelihood = -9216.4461  (not concave)
Iteration 14:  log likelihood = -9216.4461  (not concave)
Iteration 15:  log likelihood = -9216.4461  (not concave)
Iteration 16:  log likelihood = -9216.4461  (not concave)
Iteration 17:  log likelihood = -9216.4461  (not concave)
--Break--
r(1);

end of do-file

--Break--
r(1);

--
Jessi Bishop-Royse

http://sites.google.com/site/wakullagirlssoccer/
http://sites.google.com/site/jessicacbishoproyse/Home

"Without a struggle, there can be no progress."
Fredrick Douglass

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--
Partha Deb
Professor of Economics
Hunter College
ph:  (212) 772-5435
fax: (212) 772-5398
http://urban.hunter.cuny.edu/~deb/

Emancipate yourselves from mental slavery
None but ourselves can free our minds.
	- Bob Marley

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