Dear All,
I am struggling with a problem implementing Difference in Difference after doing psm - matching. Below is my initial data. Would be grateful for any help/suggestion i can get.
The problem :
If my understanding is ok, then the interaction term highlighted below is given by : ( 1 1) which is omitted but (0 1 ) which is the year fixed effects has a coefficient. Can someone please explain how i can address this problem I should have only the interaction term coefficient and the other firm-fixed and year-fixed effects should have been omitted by Stata. Is there a problem with my data or the process?
The command i am using for psmatch is as follows :
where treated is a dummy which takes value of 1 = for treated firms , 0 = control sample
in_id is the industry classification
then i use the following commands
after dropping the variables, I only use the new "matched_sample" containing the treated and control and run the fe regression using the following code
I am struggling with a problem implementing Difference in Difference after doing psm - matching. Below is my initial data. Would be grateful for any help/suggestion i can get.
The problem :
If my understanding is ok, then the interaction term highlighted below is given by : ( 1 1) which is omitted but (0 1 ) which is the year fixed effects has a coefficient. Can someone please explain how i can address this problem I should have only the interaction term coefficient and the other firm-fixed and year-fixed effects should have been omitted by Stata. Is there a problem with my data or the process?
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input double WACC byte BoardSize double ACI byte PBI double HRscore float(Size NCF LEV BS)
5.63347003345844 . . . . 19.290005 15.484914 21.705027 .
4.59750967526028 . . . . 19.128357 . 12.320194 .
3.77746605445323 . . . . 19.16905 17.13997 20.359585 .
6.74970390474236 . . . . 19.516685 . 13.391864 .
5.68245315193138 6 100 0 0 19.72842 17.977201 3.763319 1.7917595
. 6 100 0 0 19.929853 14.36767 3.090212 1.7917595
. 6 100 0 0 21.36582 20.19997 2.491998 1.7917595
11.0367841629065 11 100 1 2.94117647058823 24.926525 . 19.92407 2.397895
7.42778730583102 10 100 1 5 24.942846 21.04099 27.93765 2.3025851
6.85336268326181 11 100 1 45 25.07941 . 25.16291 2.397895
8.88741714566398 12 100 1 42.2413793103448 24.99975 . 25.280954 2.484907
8.83297672162851 11 100 1 67.9487179487179 24.97898 . 22.765116 2.397895
8.91684464693637 12 85.7143 1 63.9784946236559 24.754173 . 27.159397 2.484907
1.63851168926627 8 85.7143 1 80 24.471216 . 39.79356 2.0794415
3.03482720075186 12 57.14 1 0 23.05303 . 15.667521 2.484907
4.50144015921441 11 75 1 0 23.096205 . 14.724265 2.397895
5.28739191721452 11 75 1 88.5135135135135 23.062994 . 13.7408 2.397895
7.15192349252946 11 75 1 88 23.12607 . 11.705832 2.397895
5.5585411628984 11 100 1 88.034188034188 23.171474 . 11.78576 2.397895
3.04419067939655 11 100 1 87 23.228857 . 11.609084 2.397895
6.33456036571886 11 100 1 90.9340659340659 23.21931 . 9.526661 2.397895
3.53638060476357 . . . . 20.39232 18.69183 29.726347 .
4.01970293821479 . . . . 20.31512 16.61693 34.305134 .
7.86900127648993 . . . . 20.34547 16.639326 35.789616 .
11.8281471985644 . . . . 20.37166 . 34.88611 .
10.2352673420125 12 75 1 57.5757575757575 20.29175 17.207869 49.97998 2.484907
3.34899955302836 15 62.5 0 0 21.36102 . 26.03443 2.70805
3.29601028570595 15 62.5 0 0 21.49841 . 25.502306 2.70805
4.74315407253743 15 66.6666666666667 0 0 21.425535 . 22.80241 2.70805
5.2652908138888 15 66.6666666666667 1 7.14285714285714 21.44742 . 19.540096 2.70805
4.60120100222842 14 66.6666666666667 1 5.68181818181818 21.492495 . 16.543085 2.6390574
4.53001837432825 15 100 1 2.67857142857142 21.556454 . 15.46859 2.70805
8.37286533639052 14 100 1 5.46875 21.49952 18.30185 20.606524 2.6390574
3.9967319373643 15 62.5 1 0 25.52318 . 12.163132 2.70805
4.07545065145723 15 50 0 6.57894736842105 25.4632 . 10.195136 2.70805
6.5767930128793 18 50 0 6.87830687830687 25.44055 . 9.698236 2.890372
6.58129092939593 18 50 0 79.7665369649805 25.629623 20.219606 7.257854 2.890372
5.24778884851918 19 90.9090909090909 0 79.4212218649517 25.66573 23.290575 7.384805 2.944439
1.7292543181647 18 90.9090909090909 0 79.054054054054 25.74846 22.74264 7.761476 2.890372
3.99441798383061 19 91.6667 0 79.3991416309012 25.835007 23.47293 7.398479 2.944439
1.96303569644967 . . . . 21.486885 . 16.922829 .
1.33344452400725 . . . . 21.55791 15.956283 21.119514 .
1.8212809513979 . . . . 21.5828 . 17.840862 .
5.40964446472065 12 33.3333333333333 1 22.5 21.78272 18.490654 17.457354 2.484907
6.08052050637608 12 33.3333333333333 1 62.3931623931623 21.85941 . 15.020727 2.484907
4.88707972509067 12 33.3333333333333 1 87 22.022076 18.617579 25.24518 2.484907
3.86207510770832 12 28.5714 1 88.4615384615384 21.97873 . 21.54509 2.484907
3.47951201277907 . . . . 22.023714 . 33.483242 .
3.15032957834524 . . . . 22.10555 . 35.240864 .
3.91079106323129 . . . . 22.182623 17.921982 36.383183 .
3.97224000882967 14 66.6667 1 8.72093023255814 22.281355 18.233013 36.830048 2.6390574
3.04355256545648 13 66.6666666666667 1 5.24193548387096 22.40139 18.572319 36.28779 2.564949
1.93760250678369 14 71.4285714285714 1 3.10734463276836 22.4963 16.862696 35.616444 2.6390574
3.77812400896733 12 71.4285714285714 1 43.7799043062201 22.64317 20.34605 41.45663 2.484907
3.81550763619072 . . . . 21.588285 . 30.726835 .
4.48532388440522 . . . . 21.603155 . 28.34304 .
5.64986515558811 . . . . 21.688456 . 21.99808 .
5.97269940157363 14 88.8888888888889 1 56.4814814814814 21.638464 . 15.32073 2.6390574
4.45585806255746 14 88.8888888888889 1 54.1436464088397 21.6906 . 18.007689 2.6390574
2.86782669461171 14 88.8888888888889 1 54.1860465116279 21.86144 19.62441 31.450516 2.6390574
4.77191483896722 14 100 1 52.1276595744681 22.1495 20.849983 37.292732 2.6390574
3.64405319956127 18 62.5 1 17.4242424242424 26.002846 . 13.858513 2.890372
4.03595810120947 17 84.6153846153846 1 16.1184210526315 26.0203 . 12.743857 2.833213
4.34280230051186 17 84.6153846153846 1 70.6349206349206 26.061895 . 10.94178 2.833213
4.68333507986634 17 84.6153846153846 1 65.1750972762645 26.119886 . 9.670925 2.833213
3.12112557414141 19 84.6153846153846 1 64.6302250803858 26.190447 21.83939 11.310903 2.944439
1.44703655634591 18 84.6153846153846 1 63.7837837837837 26.22735 20.682444 11.392248 2.890372
4.41401163386838 18 84.6153846153846 1 60.0858369098712 26.348703 21.72658 10.097959 2.890372
2.64366594021844 . . . . 21.36231 . 59.533 .
2.37727843344038 . . . . 21.44954 18.478855 60.49601 .
3.76038796570812 . . . . 21.54696 16.542055 55.71561 .
4.8265556398599 . . . . 21.54404 . 49.60236 .
4.10273342109141 8 83.3333 0 67.3387096774193 21.72395 19.19361 50.35373 2.0794415
2.95826422433188 8 83.3333 0 60.7344632768361 21.86675 17.746964 47.21852 2.0794415
5.39648729751116 4 83.3333 0 72.0095693779904 21.861935 17.666084 46.62478 1.3862944
2.95367269037017 16 66.67 1 8.92857142857142 22.841496 20.36164 43.67682 2.772589
3.26454703911456 16 60 1 0 22.840067 . 40.87607 2.772589
4.64689117992175 16 60 1 0 22.79558 . 35.295135 2.772589
4.30926003400701 15 63.6363636363636 1 0 22.75644 . 33.177284 2.70805
3.03952500737106 15 63.6363636363636 1 0 22.852747 . 44.99614 2.70805
1.18342143136046 16 63.6363636363636 1 65.7342657342657 22.861685 . 49.82801 2.772589
2.23560561863016 15 70 1 63.1578947368421 22.82886 . 41.37491 2.70805
4.9357851097579 9 50 0 51.3071895424836 22.51423 . 10.959435 2.1972246
5.5220516138626 9 83.3333 0 46.3483146067415 22.47733 . 8.364236 2.1972246
6.59904025791957 9 83.3333333333333 0 42.4170616113744 22.547586 . 9.307668 2.1972246
7.00387763412047 9 60 0 93.5251798561151 22.5583 18.716719 13.947094 2.1972246
7.21026579175758 9 60 0 92.9230769230769 22.65748 . 20.41698 2.1972246
2.96832119557709 9 60 0 92.8741092636579 22.70207 . 22.388277 2.1972246
6.59576776660252 9 75 0 92.5162689804772 22.677246 . 21.723436 2.1972246
2.59260535411755 . . . . 22.595253 . 26.02289 .
2.38573191894073 . . . . 22.603724 . 23.913673 .
3.63722066654162 . . . . 22.588106 . 18.413918 .
5.33260703890684 15 42.8571 1 93.8016528925619 22.78137 . 14.485832 2.70805
4.43926440701282 17 37.5 1 92.6174496644295 22.82601 . 12.977798 2.833213
2.50261676142696 17 37.5 1 92.6229508196721 22.847406 . 14.74234 2.833213
5.32397592528207 15 28.5714 1 92.5233644859813 23.13379 . 9.852062 2.70805
3.90359946748709 15 44.44 1 85.4651162790697 24.24535 . 16.36095 2.70805
4.50664545413555 16 66.6666666666667 1 85.0515463917525 24.20954 20.446194 16.461548 2.772589
6.16715102896599 16 66.6666666666667 1 78.5046728971962 24.192495 . 16.429995 2.772589
8.01513239590314 15 66.6666666666667 1 75 24.175663 17.111347 18.957436 2.70805
end
Code:
psmatch2 treated ROA Beta NCF Size LEV in_id , out(WACC) n(1) caliper( 0.01) logit
in_id is the industry classification
then i use the following commands
Code:
drop if _pscore==. drop if _weight==. tab treated
Code:
xtreg WACC i.treated#i.treatment BGD PBI HRscore ROA Beta BS Size NCF LEV i.in_id i.year if matched_sample ==1 , fe robust
Comment