Hope my message finds you well.
I am trying to construct an estimated propensity score distributions, something just like the following table: Array
However, I couldn't get similar results of the estimated propensity scores between the treatment group and the control group just like the above table. Do you think anything wrong with my code at below? I will be really grateful if you can enlighten me on this matter! Many thanks for your time in advance!

The code I use is here:
Code:
logit interest C1 C2 C3 C4 C5 C6 C7 C8 i.sic2 i.year, cluster(gvkey) predict pscore, pr psmatch2 interest, pscore(pscore) outcome(outcome) common logit noreplacement neighbor(1) sum pscore if _support ==1 & _treated == 1 /* Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- pscore | 9,068 .6896252 .1012362 .103979 .7887203 */ sum pscore if _support ==1 & _treated == 0 /* Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- pscore | 9,068 .7502024 .1615531 .1046714 .9877867 */
----------------------- copy starting from the next line -----------------------
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long gvkey float year double outcome float(interest C1 C2 C3 C4 C5 C6 C7 C8) 164369 2008 2.7151388251357647 0 4.941493 2.3397484 .04949314 .0011388769 .6228577 2.1972246 3.399774 .3348027 6972 2010 1.877890721292218 0 8.228337 1.2218844 .06362372 .0021731863 .2981712 3.555348 9.990268 -.11383753 121080 2011 5.789910082736212 0 5.718251 .3230231 -.005164733 .2059618 .10280184 2.772589 .28878707 .034578934 25880 2006 .9129787190630863 0 7.555758 2.5215795 .10379408 .06224208 .11737715 2.772589 6.100691 .07997912 2220 2013 -.517168168559228 0 8.128228 1.1787469 .022955095 .1095001 .17968656 3.6888795 11.061206 .030667856 13286 1996 1.4279352938831034 0 3.6820815 7.668733 .2294294 .00013128913 .7831307 2.397895 8.671361 .4283204 2220 2016 -.05752226015445347 0 8.2559595 1.5130007 .007304239 .07458527 .2191921 3.7612 11.061206 .024130935 12478 2005 .03850207508012066 0 6.892303 1.301922 .067274585 .21416065 .006970892 3.465736 1.593584 -.06859488 63763 2010 .8533218931592794 0 5.622948 2.2000134 .11763112 .0004418873 .1795818 2.772589 15 .05194013 22761 2006 5.842759298255464 0 1.9624863 1.1138963 -.05550091 .02257934 .27188423 1.7917595 1.6623433 .06516982 6314 1994 -2.1405365537128573 0 4.7000256 .8469395 -.06208276 .6435176 .009877217 2.70805 10.472455 -.027114363 8062 1987 -.6311820936159533 0 5.20028 .7457203 .04536656 .3020717 .1689802 3.3322046 5.654603 .20076445 1410 2017 .005293535473093136 0 8.246067 1.0301005 .0009966951 .2999979 .016471699 4.0253515 5.578954 .06004237 147660 2003 1.240827751938839 0 5.899105 2.1979485 .08132467 .00049899204 .4351308 1.94591 .26086003 .24689575 142230 2003 1.0000000000000002 0 7.085587 1.6235653 .07380481 .2938826 .10009844 1.7917595 3.816594 .1166529 12548 1992 -1.5484953353502962 0 3.222589 1.7095604 .07531981 .005524733 .28362492 1.94591 3 .1098906 1618 1992 1.3546456227677068 0 5.126283 .6906942 -.04053685 .6301395 .02792921 3.465736 2.2422137 .05459485 126718 2009 -.6762281847753096 0 4.850224 .29697958 -.035767674 .6034774 .13233256 2.564949 3.358122 -.219864 10386 2003 -.5724263785967659 0 7.652451 .6231297 .00004748789 .31794265 .16364327 3.988984 7.965487 .35362405 4100 1987 2.8374141059872247 0 4.0045295 3.544812 .1480509 0 .28671187 1.609438 7 .5537217 4100 1991 .16590176391269484 0 5.612796 2.2755733 .12359214 .00955559 .5504091 2.1972246 7 .19899605 12455 1994 -2.335805594663573 0 3.050268 2.909669 .11812887 .05695213 .28961697 2.3025851 2.918099 2.674445 5783 1994 1.334172879918378 0 6.901434 .9605718 .04064813 .3848199 .0021555824 2.564949 7.613435 .1828545 10321 1994 1.6852743623384034 0 1.693595 1.386537 -.063798495 .03129403 .13200957 2.484907 1.7443675 .14139381 3760 1993 5.282551401801539 0 6.764265 .6050124 -.06981066 .3103237 .22157286 3.218876 14 -.034121692 8280 1985 .7756861318876593 0 4.5982666 1.0734 .05738481 .2691238 .31372845 2.833213 7.632016 -.032054167 4819 1987 .643421873563806 0 4.4029927 1.0348552 .04952567 .2990212 .10289492 2.70805 2.60677 -.05939956 2950 1998 -2.099009382845761 0 5.910829 1.901487 .04137806 .416695 .005625833 3.367296 4.921085 .5363843 141469 2012 -1.2765829637626536 0 4.893981 1.0609934 .017754937 .09143824 .15493242 2.70805 10.734857 .02119158 6158 2007 .18819759100102595 0 7.313248 .8967813 .0007933111 .3998277 .0416355 3.218876 13.441359 .06955912 29647 1995 1.8240852097892408 0 6.007872 1.2666638 .04310444 .22275054 .005159647 1.3862944 1.654253 .2308718 7346 1984 -.5516785330456541 0 2.953607 1.674413 .14169492 .0884546 .02190352 1.7917595 4.2080507 .2397838 13452 1996 -.2829430684776136 0 5.047648 1.26331 .08687105 .3196473 .017583646 2.397895 7.90572 .02865502 6845 2008 1.706167496046592 0 5.470959 .4390272 -.02118744 .07813974 .0574703 3.73767 6.25397 -.09790082 12884 1994 3.3081934308885663 0 5.234723 1.5790328 .06271413 .2024646 .6812982 2.3025851 3.26822 .368917 9355 1988 .7651523675933984 0 5.577637 .8157543 .027412023 .5852877 .011609175 3.178054 .9487611 1.7504476 141363 2012 -.3757030361910363 0 8.632856 1.0189757 -.04037278 .4215336 .017606882 2.772589 6.548636 -.0942289 6550 1998 4.926316194082722 0 5.819979 .7061247 .036380634 .467241 .0003264434 2.833213 5.90115 .0867041 8655 1999 1.827120168761847 0 6.854558 1.4133515 .02334335 .38783625 .006609414 3.2580965 13.55777 .1712541 25433 1995 -4.992065538675761 0 2.3066766 1.5072204 -.5023404 .0088542355 .5208644 1.609438 4.086528 .33311325 141359 2005 .6042671843277878 0 5.767577 2.953095 .1753054 0 .29003134 2.1972246 6.7105 .6374717 10597 2003 .17320155669581272 0 4.952865 .4839879 .029029524 0 .29085323 3.988984 11.351412 .2449137 11559 1993 .03550138637232053 0 7.516976 1.1474441 .010746613 .23716825 .0995379 3.713572 2.874323 .23926194 62962 1998 -.06836517951702736 0 2.995982 .5740565 -.19660085 .0846395 .0002013987 1.94591 1.5381507 .05342843 3015 1997 .5407504496050896 0 7.708154 .742048 .04199595 0 .674591 3.871201 13.56437 -.0022525473 20800 1997 3.0202639517334986 0 2.03901 6.654783 -.5678771 .06894367 .5133411 2.397895 3 -.03087886 12656 1992 2.7167743964461275 0 4.4878607 .9384832 .06674838 .3178399 .011660726 2.0794415 9.716379 .07247717 11670 2001 .4054602860864813 0 5.604614 .3170861 .01632085 .1950897 .3549252 3.73767 5.004245 .1746613 10534 1989 -.29408472401995406 0 1.9644514 1.5883465 .070396855 .2487971 .006450708 1.94591 6.120647 .1604076 12171 1989 .4475373897698969 0 4.484843 1.194007 .08866156 .2218785 .020121133 1.609438 2.4515684 .6543449 112876 2018 .18408873257062738 0 10.223394 1.40257 .073238805 .3276201 .007198551 3.178054 1.1968392 .24939555 6297 1993 -.5737006717179509 0 4.831836 3.099242 .14519176 .01878736 .361102 2.484907 4 .1289932 270281 2009 .49704487735235636 0 9.486707 1.0563649 .005593511 .4374549 .03871228 2.1972246 3.465312 -.4217348 29734 2003 .7558095130050375 0 6.244322 1.0166404 .01147783 .4594853 .0002013987 2.484907 4.671303 .1012119 10983 1982 .9267758981646528 0 8.986368 .5637748 .06677045 .3177047 .015196467 3.496508 8.827681 -.006628839 178765 2013 -3.8000673628476234 0 6.787039 3.1649165 .2335321 .03629139 .7140626 2.1972246 1.618597 .12650187 23224 2005 -1.2765541743166589 0 6.060804 2.98549 .10156745 0 .2437115 2.833213 2.6716316 .016655413 178973 2018 1.0000000000000002 0 6.633724 1.0802661 -.009271243 .4217074 .07022943 2.70805 2.424366 -.008360537 117862 2003 -.1200149711693943 0 6.883873 .8158029 .02806227 .27518675 .11071283 2.0794415 7.808562 .074196294 8726 1992 1.4031565384242957 0 6.009837 4.97575 .19349954 .003206385 .26380098 3.496508 4.1970882 .005819848 113301 1999 -.6295955303011547 0 6.696267 1.1947258 .01014358 .2225079 .0375226 1.3862944 .7019444 .2904366 60939 1999 .3633735999209664 0 5.301433 .5543429 .026671784 .5007171 .012844924 1.7917595 .6268463 .12329298 162957 2012 -.8926926472680357 0 8.695122 1.1103216 .06419807 .4148821 .2132012 2.564949 8 -.04425912 8087 2015 .31760857281833105 0 4.2574835 .8351458 -.015148941 0 .3109214 3.583519 2.407346 .05896406 205942 2016 1.6936797425487313 0 9.488034 1.3349293 -.008671584 .330768 .16759847 2.890372 8.8295765 .2745142 11118 1980 .39588405378917735 0 4.154169 .7959328 .10300004 .04441804 .10416176 3.0445225 3 .21717165 4162 2002 -2.527073165381847 0 4.6277237 1.0857344 .005719537 .010841963 .2352734 3.0445225 4.7057686 .07809438 3639 2017 .8323803073119759 0 7.197649 1.2259645 -.00838818 .15550914 .05131914 4.060443 7.485832 .016553724 13839 2002 -1.3493559604081735 0 6.765648 1.2106855 .0417658 .2568762 .03838032 2.833213 4.0570436 .05630494 8512 1982 1.2833095706234448 0 3.6390324 .6913218 -.014925766 .6513926 .22590987 2.1972246 8.032617 .15929507 10733 1998 -.036367867752724714 0 4.199245 .9737098 .0719431 .13354577 .023815956 2.833213 3.0060894 .13365921 157855 2006 -3.6049274683973564 0 6.074769 9.17629 .06549526 .0001760921 .4774364 2.0794415 5.348934 .7568079 24926 2000 2.460762609222196 0 7.208655 .7901936 .07164522 .270818 .003884317 2.3025851 1.7541965 .14230157 141001 2011 -.26112835283521346 0 7.655725 1.203201 .033086967 .12148739 .15468085 2.564949 4.425479 .27533144 6509 1986 3.038172297867755 0 4.503337 1.1647513 -.08243097 .3635525 .1364512 3.135494 5.940967 -.4822545 4413 1990 0 0 1.7833912 1.457815 .05798319 .1808854 .04941177 2.833213 6.909463 .12959771 25598 1993 3.1268423190733743 0 5.010028 8.734419 .1702166 .0007209585 .5337105 1.94591 2.0281293 1.1652509 61746 2003 1.3519640644251643 0 4.835059 1.3555102 -.03924638 .4134524 .00361553 2.1972246 6.465073 .028999336 5987 1989 .3326664641131246 0 5.601879 .6404728 -.05515714 .7394778 .12324359 3.2580965 7.17854 .06281358 13346 1989 -1.8429487395643784 0 .8989411 .7309195 .25813144 0 .05738706 1.3862944 4.6459155 .4406566 10642 1983 2.955520579430436 0 3.627084 .8805215 -.09906124 .5468095 .02534372 2.484907 .26086003 2.0021212 6124 1989 -.4657731215029005 0 5.462488 .3980898 -.04055023 .4812466 .06855395 2.0794415 6 -.02651681 3184 2000 2.950774344874451 0 3.417333 .7414843 -.26348072 .7741179 .027125426 3.178054 1.490267 .3800611 1618 1990 1.8152088439899314 0 5.271121 .6823962 -.012043117 .6387486 .018686354 3.4011974 2.2422137 -.07288223 109919 2015 .9999999999999998 0 5.748491 .5268932 .02440735 .0004537333 .12157135 2.995732 8.775725 -.009178414 31144 1999 .8289155340810239 0 4.0015163 3.707146 .04352517 .007103729 .4659022 1.94591 2.2512262 .11281387 145042 2006 .07738255015280415 0 5.601598 3.898108 .09466919 0 .11135683 2.397895 6.297567 .16519707 177943 2015 3.2008980142755936 0 5.914851 1.575021 -.02960332 .17308043 .09190308 2.6390574 8 -.010837157 12075 1992 .8837298529231171 0 4.326699 1.5705885 -.06182789 .3569526 .13526832 2.0794415 1.9482907 .017411329 23877 2009 .562523516027787 0 9.007799 .6359742 .02967 .307878 .2278397 2.995732 3.896391 .16702527 7107 1997 2.5755239254761273 0 5.537684 1.2188137 .06389887 .18893585 .008327791 2.70805 8.0002165 .17728986 2788 1988 2.3814026175161627 0 3.93847 .6397156 .04894819 .210334 .006213479 1.7917595 6.542931 .09362514 270281 2007 2.354103030119429 0 9.129959 1.8445626 .09894737 .2081028 .04779043 1.94591 3.465312 .5198111 4311 1985 1.8203060563233397 0 5.284553 .6230394 .027029494 .16992055 .11720216 1.609438 3.778731 .2325612 140679 2002 1.5022829437080305 0 4.1027756 1.602758 -.05483573 0 .6827196 1.94591 1.0943761 -.07306614 140679 2001 1.4009208112871445 0 4.09276 5.623951 .02181788 .00021074327 .7991487 1.7917595 1.0943761 .4224342 24038 1995 -2.111551005597044 0 5.281237 3.690279 .025262825 .06164507 .06741162 1.94591 4.3626614 .16775423 25749 2018 .02967767976258312 0 7.896497 1.474142 .05867364 .2777254 .01556113 3.3322046 2.0159633 .29469892 63763 2005 2.155106933492148 0 5.308787 2.890524 .12442543 0 .28867173 2.397895 15 .1762503 1706 2011 .2809878186922796 0 7.628849 .9257875 .05424225 .27594712 .021501146 3.637586 6.711708 .2454227 end
0 Response to How to construct the distribution of propensity scores calculated from the pre-match conditional logistic regression?
Post a Comment