Hi,
I would like to export summary statistics (two sample t-test of the difference of the two groups, as well as mean and sd by groups) using asdoc for a number of variables (lnassets roi lev cagr vol cfvol lncash delist), based on the dummy variable BClaw. BClaw is equal to 1 if the state that firm (gvkey) is incorporated in (incorpn) has passed a certain law. Here is an example of my data:
The code I have been trying it with looks as follows:
which returns the error message:
if I simply do
it seems to work but the output contains a lot more information than I want.
Ideally I would do the above with a loop, and if possible clustering the s.e.'s around incorpn, as such:
But I would be happy if I could get the first version to work. Any help would be appreciated!
Thanks
I would like to export summary statistics (two sample t-test of the difference of the two groups, as well as mean and sd by groups) using asdoc for a number of variables (lnassets roi lev cagr vol cfvol lncash delist), based on the dummy variable BClaw. BClaw is equal to 1 if the state that firm (gvkey) is incorporated in (incorpn) has passed a certain law. Here is an example of my data:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long(gvkey incorpn datadate) float(BClaw lnassets roi lev cagr vol cfvol lncash delist) 1003 9 8400 1 1.7284646 .1649503 .2224787 . . . -1.1615521 0 1003 9 8765 1 2.1434722 .1231094 .14069645 . . . .7045816 0 1003 9 9131 1 2.1091218 .04696032 .11527728 . .3243487 .1695239 -1.3205066 0 1003 9 9527 1 2.638343 .016869193 .33595425 35.431084 .22307177 . -5.298317 0 1003 9 9892 1 2.680062 .0543672 .2579871 19.58573 .13903955 .05188768 -1.4229584 0 1003 9 10257 1 2.7752104 .011594565 .3433487 24.860825 .3012686 .06689375 -.7444405 0 1003 9 10623 1 2.789937 -.4814496 .4748157 5.183002 .25959867 .11679138 -1.1973282 1 1003 9 10988 1 2.313426 -.02186171 .4476209 -11.503928 . . . 0 1004 9 6360 1 3.818811 .03953897 .4840834 . .447139 . .6941467 0 1004 9 6725 1 4.034276 .035662454 .474585 . .3880906 . .9764447 0 1004 9 7090 1 4.1619096 .04681123 .4885971 . .4276877 . .9321641 0 1004 9 7456 1 4.3007894 .05044876 .46216005 17.428518 .4554453 . 1.1226543 0 1004 9 7821 1 4.419744 .04340656 .467108 13.71092 .4464859 . 1.1098819 0 1004 9 8186 1 4.73315 .010778422 .3832191 20.97496 .4508259 .14559905 .5590442 0 1004 9 8551 1 4.7121215 .025115017 .3120642 14.695506 .43949175 .03337184 -.0020020027 0 1004 9 8917 1 4.921644 .03269741 .11526802 18.210882 .2905666 .01543511 .18481843 0 1004 9 9282 1 5.046035 .05837006 .18887423 10.99277 .2757429 .04981864 .8006554 0 1004 9 9647 1 5.289715 .058027 .2869578 21.23145 .3445867 .07587934 1.2610146 0 1004 9 10012 1 5.459973 .06534065 .20540556 19.655066 .3224897 .033278447 .958967 0 1004 9 10378 1 5.652307 .07451535 .2549553 22.3959 .4922575 .06017629 . 0 1004 9 10743 1 5.876029 .06962577 .26930535 21.5843 .2821075 .005485314 1.5184186 0 1004 9 11108 1 5.962347 .06603247 .2732156 18.229586 .3325457 .01752719 1.3972343 0 1004 9 11473 1 5.940061 .03895431 .22490117 10.06688 .5263988 .04419932 .4401886 0 1004 9 11839 1 5.979774 .02534457 .23353425 3.518675 .3404748 .02192023 .8109302 0 1004 9 12204 1 5.900311 .0007750219 .25009653 -2.0466413 .28526157 .01651055 .81315 0 1004 9 12569 1 6.034586 .02273326 .27847165 3.2010174 .22665544 .035685968 2.8944745 0 1004 9 12934 1 6.054003 .02457176 .28509632 2.505153 .2345718 .03266471 3.1129375 0 1004 9 13300 1 6.081867 .036569934 .27353454 6.238753 .27757648 .015314956 3.514705 0 1004 9 13665 1 6.272092 .04347752 .2233678 8.238668 .3334143 .005712112 3.9455545 0 1004 9 14030 1 6.508111 .05317504 .2650714 16.342669 .199219 .04456652 2.8461876 0 1004 9 14395 1 6.588418 .05734831 .2495892 18.39426 .4494495 .011976158 2.1102133 0 1004 9 14761 1 6.607998 .04745357 .27903044 11.847786 .534973 .02221332 .2159175 0 1004 9 15126 1 6.553725 .02640293 .2758964 1.5320748 .5737165 .0482364 2.6253204 0 1004 9 15491 1 6.565545 -.08298942 .36641 -.7595075 .4977813 .06240025 3.541597 0 1004 9 15856 1 6.531783 -.018074017 .3741715 -2.508516 .780392 .02444072 3.372592 0 1004 9 16222 1 6.564267 .004940137 .3553656 .3520143 .5960131 . 3.713816 0 1004 9 16587 1 6.596095 .021104025 .3153435 1.0235178 .42082205 .018312065 3.7014995 0 1004 9 16952 1 6.886347 .035923906 .3278083 12.545578 .3538446 .027473735 4.801871 0 1008 6 8917 0 .4317824 -.5746753 .05714286 . .5316426 .7218414 -2.0402207 0 1008 6 9282 0 -.027371196 -.55806786 .1839671 . .45749855 .005388222 -1.5232602 0 1008 6 9647 0 -.03562718 -.822798 .03626943 . .7069297 .1002469 -1.1394343 0 1009 9 7974 1 3.4354055 -.20596573 .5390736 . . . -1.1647521 0 1009 9 8339 1 3.246063 -.2466036 .6598155 . .2062948 . -1.214023 0 1009 9 8704 1 2.089392 -.3690594 .8872524 . .54315466 .1629517 -1.4481697 0 1009 9 9070 1 2.182562 .24873154 .7358214 -34.138393 .3064029 .072737806 -1.0216513 0 1009 9 9435 1 2.3560312 .2854299 .5620438 -25.671616 .3166397 .05993561 -2.5383074 0 1009 9 9800 1 2.650421 .18368644 .6327683 20.5639 .4450549 .06218425 -1.3823023 0 1009 9 10165 1 2.853247 .06688192 .6534248 25.05226 .4495864 .04535501 -6.214608 0 1009 9 10531 1 2.78606 .09280385 .4867115 15.41256 .9953872 .024326267 -.9493306 0 1009 9 10896 1 3.261514 .07528077 .633677 22.592733 .5440766 .030183265 -3.912023 0 1009 9 11261 1 3.47615 .06602752 .648647 23.07633 .6286498 .021390636 -6.214608 0 1009 9 11626 1 3.571193 .030484546 .5955454 29.915113 .6767214 .01319791 -3.036554 0 1009 9 11992 1 3.737098 .05688965 .51465124 17.178484 .5621175 .04781688 -2.4769385 0 1009 9 12357 1 4.1588364 .05253371 .5911996 25.55353 .6286818 .02405528 -.6674795 0 1009 9 12722 1 4.541282 .05175299 .6292439 38.17668 .4745038 .012205282 -1.838851 0 1010 32 6209 1 6.587965 .04684972 .3442347 . .2205974 .02286955 2.329519 0 1010 32 6574 1 6.670539 .0455249 .3435811 . .19338 .009634356 2.3657477 0 1010 32 6939 1 6.830786 .04450718 .3647299 . .22305876 .015862195 2.409644 0 1010 32 7304 1 6.924412 .04705074 .3582557 11.867963 .2194916 .022876967 2.4697084 0 1010 32 7670 1 7.011654 .04033933 .3607069 12.042136 .3019638 .014513645 2.3791757 0 1010 32 8035 1 7.123317 .03830934 .3727295 10.24231 .21827044 . 2.485906 0 1010 32 8400 1 7.0902 .027559934 .3910101 5.68183 .27717042 .015707677 2.5732226 0 1010 32 8765 1 7.067373 .0015736594 .375305 1.8746475 .3048166 .009196619 2.674631 0 1010 32 9131 1 7.059311 .027731873 .748532 -2.1109502 . .0995543 2.6055005 0 1010 32 9496 1 7.301307 .021279337 .7404664 7.290397 . .04167838 2.710647 0 1010 32 9861 1 7.306142 -.006740879 .7721482 8.284278 . .04845259 .7011154 0 1010 32 10226 1 7.579686 .00269661 .6050774 18.941116 . .15194876 2.8202474 0 1010 32 10592 1 7.595028 .007575373 .623316 10.28601 . .0235544 3.387166 0 1010 32 10957 1 7.49831 .15727852 .6786623 6.6152 . .19623864 6.025946 0 1010 32 11322 1 7.455234 -.04354649 .6556966 -4.06354 . .018232454 3.346495 0 1010 32 11687 1 7.470943 .08937106 .6549438 -4.0517874 . .02663246 5.003248 0 1010 32 12053 1 7.442173 -.010436848 .6046861 -1.8538333 . .03513778 4.384436 0 1010 32 12418 1 7.418133 .06737955 .4544579 -1.2290614 . .032858614 2.492048 0 1010 32 12783 1 7.503896 .013883533 .4523167 1.1044841 . .016176423 4.0707345 0 1010 32 13148 1 7.608771 .04181962 .50932634 5.710376 . . . 0 1010 32 13514 1 7.704632 .04164788 .4888669 10.020818 . . . 0 1010 32 13879 1 8.065045 .068022504 .56775534 20.56873 . . . 0 1010 32 14244 1 8.088654 .020630583 .5540171 17.34649 . . . 0 1010 32 14609 1 8.178471 .021692766 .55483526 17.110325 . . . 0 1010 32 14975 1 8.241308 .021056794 .5383845 6.051458 . . . 0 1010 32 15340 1 8.222312 .03913406 .5118047 4.556005 . . . 0 1010 32 15705 1 8.2167635 .021525996 .53172183 1.2846186 . . . 0 1010 32 16070 1 8.483036 .07294965 .3849465 8.39114 . . 6.477895 0 1011 39 8400 1 1.3373667 .06957207 .51509583 . . . -4.2686977 0 1011 39 8765 1 1.5452193 .007037748 .21603754 . .5762995 .027964767 -2.688248 0 1011 39 9131 1 1.771897 .011050663 .3356001 . .6184222 . -1.8773173 0 1011 39 9496 1 1.9516082 .01377841 .40823865 22.721474 .6657602 .004830414 -1.0613165 0 1011 39 9861 1 1.8721098 -.1045832 .20609044 11.512164 .7673731 .03094232 -.3797974 0 1011 39 10226 1 1.6164135 -.25878847 .28500497 -5.050762 .7141827 .04586212 -3.3242364 0 1011 39 10592 1 1.6058314 .06061823 .22581293 -10.88646 .56852126 .009239657 -2.918771 0 1011 39 10957 1 2.025645 -.10394407 .5140483 5.251068 .518975 .04730276 -1.7037486 0 1011 39 11322 1 2.0520704 -.2401079 .7569373 15.62927 .6399881 .02649085 -1.7660917 0 1011 39 11687 1 2.1656191 -.16605504 .7097477 20.514025 .672135 .04844726 .2021242 0 1011 39 12053 1 2.837498 -.12610555 .3663679 31.07739 . .04555298 2.1559396 0 1011 39 12418 1 3.2180755 -.12642114 .336229 47.50153 .6990436 .021032084 -.04814038 0 1011 39 12783 1 4.2090416 -.13668787 .17080782 97.61306 .7365833 .036273457 .5816568 0 1012 40 6148 0 1.6646833 .09102952 .05809993 . . . -4.2686977 0 1012 40 6513 0 1.8017098 .11485148 .06815182 . . . -5.809143 0 1012 40 6878 0 2.1049874 .07980992 .15401487 . .6825735 . -2.8824036 0 1012 40 7243 0 2.0775647 -.065122105 .20338134 14.75476 .6335995 . -2.703063 0 end format %d datadate label values incorpn incorpn label def incorpn 6 "CA", modify label def incorpn 9 "DE", modify label def incorpn 32 "NJ", modify label def incorpn 39 "PA", modify label def incorpn 40 "RI", modify
Code:
asdoc ttest lnassets, by(BClaw) label stat(mean sd dif) save(sumtable.doc) asdoc ttest roi, by(BClaw) rowappend label stat(mean sd dif) asdoc ttest lev, by(BClaw) rowappend label stat(mean sd dif) asdoc ttest cagr, by(BClaw) rowappend label stat(mean sd dif) asdoc ttest vol, by(BClaw) rowappend label stat(mean sd dif) asdoc ttest cfvol, by(BClaw) rowappend label stat(mean sd dif) asdoc ttest lncash, by(BClaw) rowappend label stat(mean sd dif) asdoc ttest delist, by(BClaw) rowappend label stat(mean sd dif)
asdoctable(): 3301 subscript invalid
<istmt>: - function returned error
r(3301);
<istmt>: - function returned error
r(3301);
Code:
asdoc ttest lnassets, by(BClaw)
Ideally I would do the above with a loop, and if possible clustering the s.e.'s around incorpn, as such:
Code:
foreach i in lnassets roi lev cagr vol cfvol lncash delist{ asdoc ttest `i', by(BClaw) rowappend label stat(mean sd dif) }
Thanks
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