T-stats for the differences between the groups

[Miral Ayan]

Hi all,
I am examing the impact of CO1 on CASH based on two groups: rating=1 and rating=0. How can I show the T-stats for the differences between the groups?


reg CASH CO1 SIZE MTB LEV if rating==1, vce(cluster gvkey)
reg CASH CO1 SIZE MTB LEV if rating==0, vce(cluster gvkey)

The data is as follows:
* Example generated by -dataex-. For more info, type help dataex
clear
input float(CASH CO1 SIZE MTB LEV rating)
.12091687 .125 4.893749 2.737383 .13108735 0
.06831112 .14285715 7.591894 1.374273 .31395 0
.036111083 .5714286 7.139219 1.6956043 .2335058 0
.05134182 .375 6.564065 2.416742 .034848813 0
.04732775 1 6.431653 2.1196942 .16162266 0
.0511916 .875 6.509946 5.417324 0 0
.018361308 0 5.831994 1.9431428 .32942665 0
.1536278 1 6.289034 1.9800346 .0022334317 0
.0006082928 .875 7.409697 1.364763 .23306143 0
.18827324 1 7.283099 2.963138 0 0
.1446771 .4285714 7.230247 3.282454 .13310792 0
.015834587 .125 5.461431 .9400551 .4366914 0
.09477506 .4166667 6.945963 1.1715845 .1782237 0
.06554912 .6666667 6.125753 1.8408785 .633895 0
.1173084 .1818182 6.635298 3.5334814 .11935822 0
.069483794 .44444445 6.246172 1.4312885 .031598695 0
.10616461 .44444445 6.701324 2.5673046 .10921077 0
.06542119 .44444445 8.257933 1.7399578 0 0
.04846779 1 8.810758 4.945684 .15314643 0
.5931721 1 5.524975 5.331542 .19929847 0
.10344365 .3 7.229746 3.220316 .14494099 0
.06347818 .1 7.416519 1.849982 .28008223 0
.182537 .2857143 3.5901084 2.976139 .07126529 0
.155177 .5714286 4.880291 1.3441194 .0010252983 0
.2998602 .7777778 5.090241 2.5914154 .21044888 0
.04597449 1 7.850726 3.04046 0 0
.10497367 .375 6.488877 1.4377306 0 0
.3128743 .5714286 5.930636 .9413062 0 0
.07784957 0 5.54739 1.7513213 .27408823 0
.28281048 1 6.028327 1.956732 .01115609 0
.10578655 .3333333 7.327743 3.0992904 .2628221 0
.25093266 .6363636 10.539932 2.7904634 0 0
.10991275 .2857143 7.051137 .9987242 .14729199 0
.07255518 1 5.901105 1.780056 .16563275 0
.185896 .22222222 5.474726 1.2805007 0 0
.014424815 1 5.494706 2.92044 .0210189 0
1.5569454 0 7.443853 5.152266 .29251346 0
.10232772 .375 6.215913 1.746067 .005067382 0
.3470056 .4 5.740796 2.077045 0 0
.3989806 .5 7.08365 5.187786 0 0
.032789033 .11111111 5.465627 1.283019 .12959345 0
.03244145 .2857143 6.118965 2.915381 .009245281 0
.27101597 .4285714 8.387828 2.0679002 0 0
.27874917 .4285714 7.586048 2.858652 .4116493 0
.022424307 .22222222 6.979988 1.4679502 .2344291 0
.05493304 1 7.02475 2.70925 .05888816 0
.06055946 .8888889 8.31779 3.285131 0 0
.13442199 .5555556 6.426503 6.431568 0 0
.26108497 .8333333 6.659666 1.9004854 0 0
.02980936 0 7.274133 2.2524748 .26870018 0
.04741439 1 7.750951 1.7162793 .28270435 0
.47253275 .5 5.680841 3.129558 0 0
.0450058 .22222222 5.617531 1.650422 3.633602e-06 0
.15727 .2857143 7.061335 3.2802186 .4356514 0
.010633436 .3333333 6.01943 2.4468164 .21879497 0
.04715744 1 6.53694 1.023986 .25356036 0
.059513 .14285715 7.453548 1.580548 .3295388 0
.6783629 1 5.764417 6.207991 .013493206 0
.5735517 .7142857 4.954693 5.016565 0 0
.17160875 .8 5.813366 1.5912465 0 0
.10105649 .2857143 6.899882 1.118969 .26012388 0
.0922118 .5714286 7.09262 1.9743214 .09806526 0
.31602365 1 6.775414 2.684138 .03938167 0
.006091299 .5 6.566462 1.493292 .27011886 0
.018565793 .875 7.164488 1.3516642 .1309662 0
.03959122 0 8.470424 2.0281072 .10497265 0
.518585 .25 5.840877 1.9456413 0 0
.27669203 .125 6.418923 1.48785 .04076029 0
.07256138 .875 7.037526 2.1787496 .21152993 0
.3679003 .14285715 6.811409 3.991629 0 0
.2541207 .3 5.330683 1.4844847 .3388534 0
.033213902 .8333333 5.36448 1.2495835 .16220516 0
.2134031 .625 6.380196 1.589231 .13073115 0
.09370057 .5555556 6.004368 2.0540872 0 0
.3576783 1 8.556991 1.8298573 0 0
.3836275 .25 7.766824 3.2640746 .1274006 0
.010908497 0 6.388481 1.5144162 .2812748 0
.018044302 .2857143 7.538877 2.476043 .08656622 0
.4539929 0 5.585307 2.2420118 .04831095 0
.20862257 .625 6.026945 1.8595295 0 0
.1591888 .4 7.779676 2.502292 .08024252 0
.12462124 .22222222 5.562795 2.0956876 0 0
.119145 .125 6.758038 1.247172 .08093492 0
.04178241 1 5.961155 2.0476468 .05153869 0
.0006082928 0 7.477879 2.0988166 .08485625 0
.25971466 .7142857 5.418746 2.2196326 0 0
.032127704 .5 5.863282 1.0644327 .13114242 0
.4779071 .5714286 6.059424 3.292687 0 0
. .0909091 7.155438 .8907291 .20723873 0
.08445257 .22222222 7.574622 1.1633238 .09551861 0
.1816063 1 6.139359 2.0436654 .011508207 0
.033220585 .5714286 7.872515 2.151788 .09526873 0
.05884094 .6666667 5.970887 1.1719154 .08385798 0
1.9298403 .8571429 6.491053 8.934058 .00010163573 0
.016852485 .125 7.16668 1.5480646 .27635202 0
1.028686 .8571429 5.146622 2.556668 0 0
.01931834 .25 6.02414 1.869153 .11240884 0
.02058213 .1 5.760657 1.26085 .4084338 0
.343399 .8888889 6.665572 2.2688107 0 0
.12932108 .25 6.652984 4.459769 .15481994 0
end
[/CODE]
------------------ copy up to and including the previous line ------------------

[Rich Goldstein]

your question is not completely clear and your example data have only 1 rating (all are 0) and thus are not useful; but my guess is that you want

regress CASH CO1 i.rating

[George Ford]

You can add many regressors to Rich's equation and use different vce to suit you.

If you want a different coefficient between the two groups, assuming rating is 0/1,

regress CASH CO1 rating c.rating#c.CO1  SIZE MTB LEV , cluster(gvkey)

The coefficient on c.rating#c.C01 measures the difference in effect of CO1 and the t-stat is a direct test of that difference.

[Miral Ayan]

Dear George Ford thank you so much. I got your point.