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st: Treatment effect with endogenous treatment for multinominal outcomes


From   Sawa Omori <[email protected]>
To   [email protected]
Subject   st: Treatment effect with endogenous treatment for multinominal outcomes
Date   Thu, 20 Mar 2014 15:43:13 +0900

Dear Stata list members,

I am using Stata13.1 SE and I would like to estimate  treatment effect
with endogenous treatment for 3 multinominal outcomes (policy
reversal, no reform, policy reform).  I am looking at the treatment
effect of international organizations on country's policy changes.

I hope cmp (Roodman, David. 2011. Stata Journal 11(2): 159-206)  works
for these estimates.

However, when I run the command as follows, results do not converge as
shown the following.
Can I use cmp command to estimate treatment effect with endogenous
treatment for multinominal outcomes since Roodman(2011) do not give
examples on this.  If could, is my command wrong and just the problems
of entered variables?  Any suggestions are very much appreciated.
Thank you so much in advance.


Sawa

Sawa Omori, Ph.D.

Associate Professor
Department of Politics and International Studies
International Christian University, Tokyo, Japan


 cmp ( lag_IO = lag_bankingcrisisdummy lag_ln_gdppc) (c3_credit =
lag_directedcredit  lag_IO  lag_gdpgrow
> th  ), indicators ($cmp_probit $cmp_mprobit)  cluster(countryNo)
Fitting individual models as starting point for full model fit.
Note: For programming reasons, these initial estimates may deviate
from your specification.
      For exact fits of each equation alone, run cmp separately on each.
Iteration 0:   log likelihood = -1762.3733
Iteration 1:   log likelihood = -1492.4516
Iteration 2:   log likelihood = -1489.2398
Iteration 3:   log likelihood = -1489.2371
Iteration 4:   log likelihood = -1489.2371
Probit regression                                 Number of obs   =       2921
                                                  LR chi2(2)      =     546.27
                                                  Prob > chi2     =     0.0000
Log likelihood = -1489.2371                       Pseudo R2       =     0.1550
----------------------------------------------------------------------------------------
                lag_IO |      Coef.   Std. Err.      z    P>|z|
[95% Conf. Interval]
-----------------------+----------------------------------------------------------------
lag_bankingcrisisdummy |   .7632921   .0878081     8.69   0.000
.5911913     .935393
          lag_ln_gdppc |  -.3850068   .0192198   -20.03   0.000
-.4226768   -.3473367
                 _cons |   2.266576   .1431045    15.84   0.000
1.986096    2.547055
----------------------------------------------------------------------------------------
Iteration 0:   log likelihood = -215.68587
Iteration 1:   log likelihood = -215.68587
Probit regression                                 Number of obs   =       2407
                                                  LR chi2(0)      =      -0.00
                                                  Prob > chi2     =          .
Log likelihood = -215.68587                       Pseudo R2       =    -0.0000
------------------------------------------------------------------------------
  _mp_cmp_y2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -2.099997   .0613833   -34.21   0.000    -2.220306   -1.979688
------------------------------------------------------------------------------
Iteration 0:   log likelihood = -691.41286
Iteration 1:   log likelihood = -672.32085
Iteration 2:   log likelihood = -672.10768
Iteration 3:   log likelihood = -672.10763
Probit regression                                 Number of obs   =       2407
                                                  LR chi2(3)      =      38.61
                                                  Prob > chi2     =     0.0000
Log likelihood = -672.10763                       Pseudo R2       =     0.0279
------------------------------------------------------------------------------------
        _mp_cmp_y3 |      Coef.   Std. Err.      z    P>|z|     [95%
Conf. Interval]
-------------------+----------------------------------------------------------------
lag_directedcredit |   .1833153   .0349896     5.24   0.000
.114737    .2518936
            lag_IO |  -.1169937   .0787977    -1.48   0.138
-.2714343    .0374469
     lag_gdpgrowth |   .0170111   .0070026     2.43   0.015
.0032863    .0307359
             _cons |   1.121325   .0655769    17.10   0.000
.9927968    1.249854
------------------------------------------------------------------------------------
Iteration 0:   log likelihood = -583.01649
Iteration 1:   log likelihood = -551.18079
Iteration 2:   log likelihood = -550.17504
Iteration 3:   log likelihood = -550.17195
Iteration 4:   log likelihood = -550.17195
Probit regression                                 Number of obs   =       2407
                                                  LR chi2(3)      =      65.69
                                                  Prob > chi2     =     0.0000
Log likelihood = -550.17195                       Pseudo R2       =     0.0563
------------------------------------------------------------------------------------
        _mp_cmp_y4 |      Coef.   Std. Err.      z    P>|z|     [95%
Conf. Interval]
-------------------+----------------------------------------------------------------
lag_directedcredit |  -.2926993   .0408909    -7.16   0.000
-.3728439   -.2125546
            lag_IO |   .1021243   .0863737     1.18   0.237
-.067165    .2714135
     lag_gdpgrowth |  -.0161491   .0074706    -2.16   0.031
-.0307913   -.0015069
             _cons |  -1.132346   .0686062   -16.51   0.000
-1.266811   -.9978799
------------------------------------------------------------------------------------
Fitting full model.
Likelihoods for 2407 observations involve cumulative normal
distributions above dimension 2.
Using ghk2() to simulate them. Settings:
    Sequence type = halton
    Number of draws per observation = 99
    Include antithetic draws = no
    Scramble = no
    Prime bases = 2 3 5
Each observation gets different draws, so changing the order of
observations in the data set would change
> the results.
Iteration 0:   log pseudolikelihood = -2389.7011  (not concave)
Iteration 1:   log pseudolikelihood = -2268.9075  (not concave)
Iteration 2:   log pseudolikelihood = -2247.2773  (not concave)
Iteration 3:   log pseudolikelihood = -2244.6163  (not concave)
Iteration 4:   log pseudolikelihood = -2243.2808  (not concave)
Iteration 5:   log pseudolikelihood = -2242.5749  (not concave)
cannot compute an improvement -- discontinuous region encountered
convergence not achieved
r(430);
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