Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
| From | Qing Gong Yang <qinggong.yang@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| 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 <ecofrax@langate.gsu.edu> 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 <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/ > * * 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/