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From |
"Fernando Rios Avila" <ecofrax@langate.gsu.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: Heckprob problems |

Date |
Mon, 07 Feb 2011 20:53:49 -0500 |

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 <qinggong.yang@gmail.com> 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 <ecofrax@langate.gsu.edu> 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 <qinggong.yang@gmail.com> 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/

**Follow-Ups**:**Re: st: Heckprob problems***From:*Qing Gong Yang <qinggong.yang@gmail.com>

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