Hi All,
For the following sample I run a probit model on a set of variables (x1 x2 x3 x4 i.yearmo),
but I want to calculate the probability of DV happening using the coefficients of the x variables and do not want to include the effects of i.yearmo in the post estimation. I don't think adding _b[variable]s is the most efficient and accurate approach. I really appreciate your help with it.
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(unit_id yearmonth x1 x2 x3 x4 dv)
3 648 -1.673604 -1.0363818 3.271517 3.790306 1
3 649 .4038841 -1.0363818 3.174138 3.809004 1
3 650 .562588 -1.0363818 3.425862 3.77931 1
3 651 1.291389 -1.0363818 3.412346 3.6407406 1
3 652 .8052794 -1.0363818 3.7186666 3.967077 1
3 653 -.03675416 -1.0363818 3.219872 3.798276 1
3 654 1.0632977 -1.0363818 3.466667 3.8156135 1
3 655 -.5068667 -1.0363818 2.842949 3.8489246 1
3 656 .5646673 -1.0363818 3.360119 3.63149 1
3 657 .934234 -1.0363818 3.3083334 3.7305555 1
3 658 .32183555 .7075664 3.7373564 3.793548 1
4 648 -2.053771 -1.0363818 3.267277 3.7204175 1
4 649 -.28470853 -1.0363818 3.773889 3.989841 1
4 650 .1102932 -1.0363818 3.4863095 3.9390345 1
4 651 .5085503 -1.0363818 3.5165405 3.55 1
4 652 .4732249 -1.0363818 3.5431216 3.605337 1
4 653 -.3396801 -1.0363818 3.7653334 3.3848386 1
4 654 -.8023075 -1.0363818 3.746795 3.404524 1
4 655 .524676 -1.0363818 3.7515874 3.2825396 1
4 656 -.0806455 -1.0363818 3.571839 3.4154506 1
4 657 .7601319 -1.0363818 4.146528 3.3850396 1
4 658 -.29708236 .7053064 4.07963 3.9800696 1
5 648 -.09884956 -1.0363818 4.0142856 3.9034524 1
5 649 .3637376 -1.0363818 3.627564 3.9173334 1
5 650 .5272369 -1.0363818 3.279167 3.396795 1
5 651 .4010301 -1.0363818 3.641667 3.996528 1
5 652 .03462299 -1.0363818 3.607143 3.503472 1
5 653 -.8958375 -1.0363818 3.972222 3.9333334 1
5 654 -.8420179 -1.0363818 3.8314815 3.6791666 1
5 655 -1.972452 -1.0363818 3.801587 3.4958334 1
5 656 -.8649931 -1.0363818 3.795833 3.6608696 1
5 657 -.13952091 -1.0363818 3.608696 3.4591954 1
5 658 -.035976883 .8973646 3.625 3.270988 1
6 648 .6677211 -1.0363818 3.5735295 3.5553334 0
6 649 .997246 -1.0363818 4.1704545 3.642529 0
6 650 .6545075 -1.0363818 3.142593 4.140199 0
6 651 .8990768 -1.0363818 3.4585884 4.068254 0
6 652 .4727888 -1.0363818 3.5944445 4.099575 0
6 653 -.5000833 -1.0363818 3.852193 3.8843474 0
6 654 1.8032534 -1.0363818 3.210563 3.9385715 0
6 655 -.9693494 -1.0363818 3.6065714 3.866164 0
6 656 .05690836 -1.0363818 3.5489795 3.842613 0
6 657 .463342 -1.0363818 3.4930556 3.975397 0
6 658 .16892995 .6388407 3.357471 3.988834 0
7 648 .7216926 -1.0363818 3.864368 4.069512 0
7 649 .8041119 -1.0363818 3.4160714 3.954662 0
7 650 .7418164 -1.0363818 4.1182795 4.158704 0
7 651 .8701565 -1.0363818 4.010552 4.4940276 0
7 652 .7349644 -1.0363818 4.110931 4.38009 0
7 653 .12824593 -1.0363818 4.4591713 4.4671345 0
7 654 .58063346 -1.0363818 4.526656 4.651618 0
7 655 .10464822 -1.0363818 4.379938 4.608889 0
7 656 .4740608 -1.0363818 4.61358 4.4431086 0
7 657 .4891589 -1.0363818 4.579167 4.3469625 0
7 658 .21748453 1.101524 4.451282 4.410873 0
end
[/CODE]
For the following sample I run a probit model on a set of variables (x1 x2 x3 x4 i.yearmo),
Code:
probit dv x1 x2 x3 x4 i.yearmo
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(unit_id yearmonth x1 x2 x3 x4 dv)
3 648 -1.673604 -1.0363818 3.271517 3.790306 1
3 649 .4038841 -1.0363818 3.174138 3.809004 1
3 650 .562588 -1.0363818 3.425862 3.77931 1
3 651 1.291389 -1.0363818 3.412346 3.6407406 1
3 652 .8052794 -1.0363818 3.7186666 3.967077 1
3 653 -.03675416 -1.0363818 3.219872 3.798276 1
3 654 1.0632977 -1.0363818 3.466667 3.8156135 1
3 655 -.5068667 -1.0363818 2.842949 3.8489246 1
3 656 .5646673 -1.0363818 3.360119 3.63149 1
3 657 .934234 -1.0363818 3.3083334 3.7305555 1
3 658 .32183555 .7075664 3.7373564 3.793548 1
4 648 -2.053771 -1.0363818 3.267277 3.7204175 1
4 649 -.28470853 -1.0363818 3.773889 3.989841 1
4 650 .1102932 -1.0363818 3.4863095 3.9390345 1
4 651 .5085503 -1.0363818 3.5165405 3.55 1
4 652 .4732249 -1.0363818 3.5431216 3.605337 1
4 653 -.3396801 -1.0363818 3.7653334 3.3848386 1
4 654 -.8023075 -1.0363818 3.746795 3.404524 1
4 655 .524676 -1.0363818 3.7515874 3.2825396 1
4 656 -.0806455 -1.0363818 3.571839 3.4154506 1
4 657 .7601319 -1.0363818 4.146528 3.3850396 1
4 658 -.29708236 .7053064 4.07963 3.9800696 1
5 648 -.09884956 -1.0363818 4.0142856 3.9034524 1
5 649 .3637376 -1.0363818 3.627564 3.9173334 1
5 650 .5272369 -1.0363818 3.279167 3.396795 1
5 651 .4010301 -1.0363818 3.641667 3.996528 1
5 652 .03462299 -1.0363818 3.607143 3.503472 1
5 653 -.8958375 -1.0363818 3.972222 3.9333334 1
5 654 -.8420179 -1.0363818 3.8314815 3.6791666 1
5 655 -1.972452 -1.0363818 3.801587 3.4958334 1
5 656 -.8649931 -1.0363818 3.795833 3.6608696 1
5 657 -.13952091 -1.0363818 3.608696 3.4591954 1
5 658 -.035976883 .8973646 3.625 3.270988 1
6 648 .6677211 -1.0363818 3.5735295 3.5553334 0
6 649 .997246 -1.0363818 4.1704545 3.642529 0
6 650 .6545075 -1.0363818 3.142593 4.140199 0
6 651 .8990768 -1.0363818 3.4585884 4.068254 0
6 652 .4727888 -1.0363818 3.5944445 4.099575 0
6 653 -.5000833 -1.0363818 3.852193 3.8843474 0
6 654 1.8032534 -1.0363818 3.210563 3.9385715 0
6 655 -.9693494 -1.0363818 3.6065714 3.866164 0
6 656 .05690836 -1.0363818 3.5489795 3.842613 0
6 657 .463342 -1.0363818 3.4930556 3.975397 0
6 658 .16892995 .6388407 3.357471 3.988834 0
7 648 .7216926 -1.0363818 3.864368 4.069512 0
7 649 .8041119 -1.0363818 3.4160714 3.954662 0
7 650 .7418164 -1.0363818 4.1182795 4.158704 0
7 651 .8701565 -1.0363818 4.010552 4.4940276 0
7 652 .7349644 -1.0363818 4.110931 4.38009 0
7 653 .12824593 -1.0363818 4.4591713 4.4671345 0
7 654 .58063346 -1.0363818 4.526656 4.651618 0
7 655 .10464822 -1.0363818 4.379938 4.608889 0
7 656 .4740608 -1.0363818 4.61358 4.4431086 0
7 657 .4891589 -1.0363818 4.579167 4.3469625 0
7 658 .21748453 1.101524 4.451282 4.410873 0
end
[/CODE]
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