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Re: st: Heckprob problems
From
Qing Gong Yang <[email protected]>
To
[email protected]
Subject
Re: st: Heckprob problems
Date
Tue, 8 Feb 2011 15:03:56 +1300
Thanks. in this case, there is indeed high correlation between leader and lnms.
On Tue, Feb 8, 2011 at 2:53 PM, Fernando Rios Avila
<[email protected]> wrote:
> Well, I ve been checking ur results and also simulating some examples with other data. From what i see in your last output, it seems that you have to recheck again the nature of your data.
> If u see carefully, u have a lot of problems with the model convergence, which seems to be explained because one of the parameters, "rho", is constantly reaching the lower bound of its possible values (-1). From my experience it could be that you are doing too much of a good job explaining the selection model.
> What i suggest is to check the correlation of all the variables and see if there is any suspiciously high or low correlations between your explainatory variables.
> Best regards
>
>>>> Qing Gong Yang <[email protected]> 02/07/11 8:05 PM >>>
> Thanks for this.
>
> I did use
> heckprob y1 x1 x2 x3 x4, sel (y2= x1 x5 x6).
>
> There are about 300 observations, a little bit over 200 are censored.
>
> for one regression, I estimate heckprob y1 x1 x2 x3 x4, sel(y2= x5 x6
> x1) just for the convenience of output to table, yet the estimation
> does not work any more.
>
> Here is an example
>
> .heckprob slc1 x1 leader bte counter efficiency vig divestment
> coordination if efficiency<2, sel(ncomp= x1 x2 lnms lnsmall )
>
> Fitting probit model:
>
> Iteration 0: log likelihood = -52.340138
> Iteration 1: log likelihood = -30.883946
> Iteration 2: log likelihood = -28.066265
> Iteration 3: log likelihood = -27.746348
> Iteration 4: log likelihood = -27.740815
> Iteration 5: log likelihood = -27.740812
>
> Fitting selection model:
>
> Iteration 0: log likelihood = -176.79924
> Iteration 1: log likelihood = -126.10593
> Iteration 2: log likelihood = -123.48405
> Iteration 3: log likelihood = -123.44962
> Iteration 4: log likelihood = -123.44961
>
> Comparison: log likelihood = -151.19042
>
> Fitting starting values:
>
> Iteration 0: log likelihood = -58.91751
> Iteration 1: log likelihood = -29.3684
> Iteration 2: log likelihood = -23.529911
> Iteration 3: log likelihood = -21.781926
> Iteration 4: log likelihood = -21.505145
> Iteration 5: log likelihood = -21.497738
> Iteration 6: log likelihood = -21.497731
>
> Fitting full model:
>
> Iteration 0: log likelihood = -232.35861 (not concave)
> Iteration 1: log likelihood = -199.37133 (not concave)
> Iteration 2: log likelihood = -188.63839
> Iteration 3: log likelihood = -156.78336 (not concave)
> Iteration 4: log likelihood = -148.90122
> Iteration 5: log likelihood = -145.49084
> Iteration 6: log likelihood = -144.70601
> Iteration 7: log likelihood = -144.55228
> Iteration 8: log likelihood = -144.53009
> Iteration 9: log likelihood = -144.52176
> Iteration 10: log likelihood = -144.51786
> Iteration 11: log likelihood = -144.51461
> Iteration 12: log likelihood = -144.51368
> Iteration 13: log likelihood = -144.51337
> Iteration 14: log likelihood = -144.51303 (backed up)
> Iteration 15: log likelihood = -144.51293
> Iteration 16: log likelihood = -144.51286 (not concave)
> Iteration 17: log likelihood = -144.51282 (not concave)
> Iteration 18: log likelihood = -144.51282 (not concave)
> Iteration 19: log likelihood = -144.51281 (not concave)
> Iteration 20: log likelihood = -144.51281 (not concave)
> Iteration 21: log likelihood = -144.5128 (not concave)
> Iteration 22: log likelihood = -144.5128 (not concave)
> Iteration 23: log likelihood = -144.5128 (not concave)
> numerical derivatives are approximate
> nearby values are missing
> Iteration 24: log likelihood = -144.5128 (not concave)
> numerical derivatives are approximate
> nearby values are missing
> Iteration 25: log likelihood = -144.5128 (not concave)
> numerical derivatives are approximate
> nearby values are missing
> Iteration 26: log likelihood = -144.5128 (not concave)
> numerical derivatives are approximate
> nearby values are missing
> Iteration 27: log likelihood = -144.5128
>
> Probit model with sample selection Number of obs = 294
> Censored obs = 209
> Uncensored obs = 85
>
> Wald chi2(8) = 13603.11
> Log likelihood = -144.5128 Prob > chi2 = 0.0000
>
> ------------------------------------------------------------------------------
> | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> slc1 |
> x1 | .0050248 .012672 0.40 0.692 -.0198118 .0298614
> leader | .595136 .4217066 1.41 0.158 -.2313938 1.421666
> bte | .8001224 .2163814 3.70 0.000 .3760227 1.224222
> counter | -.4242046 .0052946 -80.12 0.000 -.4345818 -.4138273
> efficiency | -.3950646 .0125419 -31.50 0.000 -.4196463 -.3704829
> vig | .2503349 .4029704 0.62 0.534 -.5394726 1.040142
> divestment | .0444567 .432335 0.10 0.918 -.8029043 .8918178
> coordination | .8577901 .3261174 2.63 0.009 .2186118 1.496968
> _cons | -1.192662 .5165695 -2.31 0.021 -2.20512 -.1802043
> -------------+----------------------------------------------------------------
> ncomp |
> x1 | .5470875 .0137725 39.72 0.000 .5200939 .5740811
> x2 | -.060799 .2821573 -0.22 0.829 -.6138171 .4922191
> lnms | .0202606 .001606 12.62 0.000 .0171128 .0234084
> lnsmalls | .0329228 .0008017 41.06 0.000 .0313514 .0344941
> _cons | -2.623737 .1526243 -17.19 0.000 -2.922875 -2.324599
> -------------+----------------------------------------------------------------
> /athrho | -16.16538 12092.39 -0.00 0.999 -23716.82 23684.49
> -------------+----------------------------------------------------------------
> rho | -1 4.38e-10 -1 1
> ------------------------------------------------------------------------------
> LR test of indep. eqns. (rho = 0): chi2(1) = 13.36 Prob > chi2 = 0.0003
>
>
> when I estimate
> heckprob slc1 x1 leader bte counter efficiency vig divestment
> coordination if efficiency<2, sel(ncomp= lnms lnsmall x1 x2 )
>
> it does not work any more.
>
>
>
>
>
> On Tue, Feb 8, 2011 at 1:01 PM, Fernando Rios Avila
> <[email protected]> wrote:
>> Well, my only suggestion is to be sure that your y1 variable is appropriatly censored, since it seems that you are not impossing any censorship on the selection equation. Perhaps what u want to do is:
>>
>> heckprob y1 x1 x2 x3 x4, sel (y2= x1 x5 x6)
>> assuming that y2 =1 when the selection holds, and y2=0 when its not observed
>> Finally, i wonder which are the variables that you were switching possitions.
>>
>> Best regards
>>
>> Fernando Rios Avila
>>
>>
>>>>> Qing Gong Yang <[email protected]> 02/07/11 6:54 PM >>>
>> I am trying to estimate a probit model with selection using Stata 9 and 10's
>> Heckprob command in the following form:
>> Heckprob y1 x1 x2 x3 x4, sel (y2 x1 x5 x6).
>>
>> I came across several problems:
>> first, the results are not stable. They change even when I just change the
>> order of the variables.
>> second, funny results sometimes with very big Wald Chi2 value and very big
>> Z values.
>> third, The maximum log likelihood estimation seems to be critised by some as
>> inconsistent too.
>>
>> Any suggestions?
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
>>
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
>>
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/