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st: RE: triprobit convergence problem


From   "Roy, Manan" <mroy@mail.smu.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: triprobit convergence problem
Date   Mon, 22 Mar 2010 10:54:56 -0500

Hi Stephen,

1) I used -triprobit- on ssc. 

2) The data is on adults 25-59 years old with at least 1 child between 5-18 years old (N=1600), and adults between 25-59 with at least 1 child between 5-10 years old (N=1027). The model is trying to identify the effect of school meal program participation on different time use categories.

3) TIME4_0, NSLP, SMEAL, male, WNonHisp South are all dummy variables

4) This exact same trivariate model with TIME4_0, however, converges with the data set with N=1027, without any options.

5) The following  output is for the data set with N=1600

(a)
 triprobit (TIME4_0 = NSLP  SMEAL male  teage  agesq  WNonHisp  South)  (NSLP = male teage agesq WNonHisp South) (SMEAL = male teage agesq WNonHisp South)  [w=eufinlwgt], difficult

(analytic weights assumed)

trivariate probit, GHK simulator, 25 draws

Comparison log likelihood = -2995.3698

initial:       log likelihood = -2995.3698
rescale:       log likelihood = -2995.3698
rescale eq:    log likelihood = -2995.3698
Iteration 0:   log likelihood = -2995.3698  
Iteration 1:   log likelihood = -2810.0233  (not concave)
Iteration 2:   log likelihood = -2798.2733  (not concave)
Iteration 3:   log likelihood = -2790.7672  (not concave)
Iteration 4:   log likelihood = -2790.6208  (not concave)
Iteration 5:   log likelihood = -2790.2056  (not concave)
Iteration 6:   log likelihood = -2789.4005  (not concave)
Iteration 7:   log likelihood = -2789.3472  (not concave)
Iteration 8:   log likelihood = -2789.1896  (not concave)
Iteration 9:   log likelihood = -2788.9758  (not concave)
Iteration 10:  log likelihood = -2788.3554  (not concave)
Iteration 11:  log likelihood =  -2788.204  (not concave)
Iteration 12:  log likelihood = -2787.9404  (not concave)
Iteration 13:  log likelihood = -2787.9003  (not concave)
Iteration 14:  log likelihood = -2787.6958  (not concave)
Iteration 15:  log likelihood = -2787.3079  (not concave)
Iteration 16:  log likelihood = -2787.1486  (not concave)
Iteration 17:  log likelihood = -2786.8424  (not concave)
Iteration 18:  log likelihood = -2786.8261  (not concave)
Iteration 19:  log likelihood = -2786.7436  (not concave)
Iteration 20:  log likelihood = -2786.6794  (not concave)
numerical derivatives are approximate
nearby values are missing
numerical derivatives are approximate
nearby values are missing
numerical derivatives are approximate
nearby values are missing
Iteration 21:  log likelihood = -2786.6747  (not concave)
Iteration 22:  log likelihood = -2786.6708  (not concave)
could not calculate numerical derivatives
missing values encountered
r(430);

(b)
triprobit (TIME4_0 = NSLP  SMEAL male  teage  agesq  WNonHisp  South)  (NSLP = male teage agesq WNonHisp South) (SMEAL = male teage agesq WNonHisp South)  [w=eufinlwgt], difficult draws(45)
(analytic weights assumed)

trivariate probit, GHK simulator, 45 draws

Comparison log likelihood = -2995.3698

initial:       log likelihood = -2995.3698
rescale:       log likelihood = -2995.3698
rescale eq:    log likelihood = -2995.3698
Iteration 0:   log likelihood = -2995.3698  
Iteration 1:   log likelihood = -2876.4216  
Iteration 2:   log likelihood = -2801.6882  
Iteration 3:   log likelihood = -2795.6717  
Iteration 4:   log likelihood = -2794.0842  (backed up)
Iteration 5:   log likelihood = -2791.9006  
Iteration 6:   log likelihood =  -2790.852  (not concave)
Iteration 7:   log likelihood = -2790.7997  (not concave)
Iteration 8:   log likelihood = -2790.7166  (not concave)
Iteration 9:   log likelihood = -2790.6944  (not concave)
Iteration 10:  log likelihood = -2790.5223  (not concave)
Iteration 11:  log likelihood = -2790.4437  (not concave)
Iteration 12:  log likelihood = -2790.4041  (not concave)
Iteration 13:  log likelihood =  -2790.375  (not concave)
Iteration 14:  log likelihood = -2790.3516  (not concave)
Iteration 15:  log likelihood = -2790.3329  (not concave)
Iteration 16:  log likelihood = -2790.3146  (not concave)
Iteration 17:  log likelihood = -2790.0194  (not concave)
Iteration 18:  log likelihood = -2789.7409  (not concave)
Iteration 19:  log likelihood = -2789.5673  (not concave)
Iteration 20:  log likelihood = -2789.1887  (not concave)
Iteration 21:  log likelihood = -2789.1792  (not concave)
Iteration 22:  log likelihood = -2789.0136  (not concave)
Iteration 23:  log likelihood = -2788.9393  (not concave)
Iteration 24:  log likelihood = -2788.8466  (not concave)
Iteration 25:  log likelihood = -2788.8227  (not concave)
Iteration 26:  log likelihood = -2788.7829  (not concave)
Iteration 27:  log likelihood = -2788.7466  (not concave)
Iteration 28:  log likelihood = -2788.6929  (not concave)
Iteration 29:  log likelihood = -2788.5878  (not concave)
Iteration 30:  log likelihood = -2788.5645  (not concave)
Iteration 31:  log likelihood = -2788.5494  (not concave)
Iteration 32:  log likelihood = -2788.5383  (not concave)
Iteration 33:  log likelihood = -2788.5355  (not concave)
Iteration 34:  log likelihood = -2788.5213  (not concave)
numerical derivatives are approximate
nearby values are missing
numerical derivatives are approximate
nearby values are missing
numerical derivatives are approximate
nearby values are missing
Iteration 35:  log likelihood = -2788.5166  (not concave)
Iteration 36:  log likelihood = -2788.5163  (not concave)
could not calculate numerical derivatives
missing values encountered
r(430);

6) Will try the other options.

Thanks!

Manan
________________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Stephen P. Jenkins [stephenj@essex.ac.uk]
Sent: Monday, March 22, 2010 7:59 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: triprobit convergence problem

------------------------------

Date: Sun, 21 Mar 2010 17:27:20 -0500
From: "Roy, Manan" <mroy@mail.smu.edu>
Subject: st: triprobit convergence problem

Hi,

I am trying to estimate triprobit models with different time
categories (as dummies) and 2 binary program participation
variables.

 I have 2 almost identical data sets, one with N=1600 and the
other with N=1000.
There are 2 time categories for which the models are not
converging. Let's call them TIME1 and TIME2.=20
TIME1 converges in N=1600 data while it doesn't in N=1000 data.
The exactly opposite case holds for TIME2.

I have tried using the technique option. However, I get the error
that this option's not allowed with triprobit.

I have also tried the difficult option, and specified different
number of draws.

Any suggestions on how I can make it work will be greatly
appreciated.

Thanks,

Manan
>>>>>>>>>>>>>>>>>>>

You should state the source of the user-written program
-triprobit- (it is on SSC, I believe)

You do not provide, as the Statalist FAQ asks, the precise Stata
commands that you typed and the output that was produced.

And, sorry, the nature of your trivariate probit specification is
unclear from what you write, in any case.

There are at least 3 other ways to estimate trivariate probit
models, and you could try them (they also allow -maximize-
options like -difficult- and -technique(...)-:

* -mvprobit- on SSC

* Generic code using a plugin (and so fast): see Cappellari &
Jenkins, Stata Journal 6(2), 2006 [article downloadable from
Stata Journal website]

* -cmp- on SSC


Stephen
-------------------------------------
Professor Stephen P. Jenkins <stephenj@essex.ac.uk>
Institute for Social and Economic Research (ISER)
University of Essex, Colchester CO4 3SQ, UK
Tel: +44(0)1206 873374. Fax: +44(0)1206 873151
http://www.iser.essex.ac.uk
Survival Analysis using Stata:
http://www.iser.essex.ac.uk/survival-analysis
Downloadable papers and software:
http://ideas.repec.org/e/pje7.html


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