Do the regression for only one company each and saving them to an excel/doc (ASDOC)

Hi everybody,

I do have a panel dataset where every company has a unique ID (-company-) and their returns (-ri). I do run my regression with STATA and fix my ID over the time period with this code:

Code:

xtset company monthly_date, monthly
xtreg ri smb_5 hml rmw cma mktminusrf logdiff_fintech_funding,fe

However, is there a method where I can run each regression by single ID. For example I want only ID == 1 and then 2 and so on

I guess/know that I have to use the normal reg command for this because holding one company fix would not make sense. The normal pooled OLS regression (with robust) would be this

Code:

reg ri smb_5 hml rmw cma mktminusrf logdiff_fintech_funding, vce(robust)

Is it now possible to run for each company a single regression and more preverably save the coef. and standard error to an doc/excel? I know ASDOC but as fas as I know ASDOC saves the whole regression to a word document, I would only need the coef. and standard error of logdiff_fintech_funding.

I could upload the data in wide format and run each regression for each company but maybe there is a way to do it in long format.

Code:

* Example generated by -dataex-. To install: ssc install dataex
clear
input double logdiff_fintech_funding float monthly_date double(mktminusrf smb_5 hml rmw cma) byte company double ri
-2.428878818318338 601   3.4  1.53  2.74  -.55  1.43 1  1.079242090318634
 4.750675543873377 602  6.31  1.85  2.01   -.9  1.67 1   7.86393930364869
-1.547824874555269 603     2  5.03  3.12   .49  1.69 1  7.512007394694056
-2.377775945715615 604 -7.89  -.08 -2.32  1.38  -.18 1 -7.985979824222703
 2.033946504409595 605 -5.56 -2.59 -4.27  -.34 -1.48 1 -6.335626764257626
 1.866966321771243 606  6.93   .13   .04   .32  2.03 1  2.881855746293185
-6.230064946559605 607 -4.77 -3.07 -1.51   .34 -2.13 1  -9.47372995462771
 5.650530448089943 608  9.54  3.71 -2.94  -.01   .39 1  6.938139934459893
  .453532778924937 609  3.88   .72 -2.23  1.46  -.16 1  -1.05813682753868
-2.051943886966359 610    .6  3.54  -.58   -.1  1.76 1 -.2466604777739863
 2.453635974122106 611  6.82  1.03  3.47 -3.44  3.44 1  14.13006373515285
-2.555251514231911 612  1.99 -2.38   .68 -1.07    .8 1 -.7969297791341908
-.3940574407002404 613  3.49  1.76  1.73 -1.76   .72 1  .5468279827189715
 .9019179029163964 614   .45  2.66 -1.16  1.21  -.03 1  .9523648032545907
-3.366182863817053 615   2.9  -.41 -2.15   .96 -1.28 1  .5892439640544306
 3.801343710610448 616 -1.27  -.69 -2.12  2.02 -1.46 1  -1.49001583687659
 .5375060083539553 617 -1.75   .09  -.26  2.16  -1.4 1 -2.242232233723338
-.9872492784416207 618 -2.36 -1.38 -1.18  2.41 -1.75 1 -3.803201280158997
 .6191782687064706 619 -5.99 -3.39 -1.58  2.79  -.23 1 -10.01027367894514
  .263701731990106 620 -7.59  -3.9  -.98  1.71   .24 1 -9.691976652945483
-1.255992779152897 621 11.35  3.72  -.96 -1.42  -.86 1  14.10180765195524
-7.328765598425764 622  -.28  -.34  -.18  1.46  1.52 1 -.7010521563359489
 6.918695219020471 623   .74  -.36  1.57   .59  2.44 1   2.80698102953636
 1.664472961319299 624  5.05  2.35 -2.14 -1.05 -1.41 1  4.812300227465119
-4.488823618117669 625  4.42 -1.54   .01  -.17  -.03 1  4.929256079524911
 2.808398174936492 626  3.11   -.3  -.06   .25   .77 1  6.134441796438536
-.1262357447864098 627  -.85  -.66   -.2   .96   .72 1 -.6589440873023984
 .4387329863579144 628 -6.19   -.2   .08  1.98  2.37 1 -5.232003502156303
 .6944331180936318 629  3.89   .99   .54 -1.48   .37 1  3.161005632020767
-.8876966737515692 630   .79 -2.74   .01   .68   .12 1 -2.194379715301951
 1.840011420252095 631  2.55   .61    .6  -.77  -.69 1  3.209782285250071
-.6554042290035031 632  2.73   .69  1.56 -1.14  1.57 1  3.744035044489593
-.2497564922306408 633 -1.76   -.8  4.16 -1.35  2.28 1 -.0112728353654932
-2.997485122381406 634   .78   .41 -1.12   .94   .93 1 -1.707135468131021
 2.968343045907073 635  1.18  1.91  3.26 -1.75   .88 1  3.042648155235065
 .2701289866054237 636  5.57   .57  1.34 -1.88  1.47 1  4.353588346029981
 .1509605075700726 637  1.29  -.35   .28  -.96   .49 1  1.795811335142117
-1.062582825088892 638  4.03    .9  -.07   .13  1.21 1  4.731694854308732
 2.455217987516834 639  1.56 -2.32   .35   .04   .39 1 -1.228710327935455
-.6101666516017357 640   2.8  2.27  1.33  -.71  -.83 1  4.940060638818191
-.8187397318572058 641  -1.2  1.33   -.4  -.47   .01 1  2.524169010641897
 .1706158923222114 642  5.65  1.81   .71 -1.43   .53 1  7.810874448103843
-.7756070164821449 643 -2.71  -.03 -2.48   .85 -2.13 1 -3.497821482444668
 1.726891305749435 644  3.77  2.72 -1.57   -.1 -1.32 1  1.662269053409684
 .3957065823815764 645  4.18 -1.57  1.36  2.83   .89 1  3.530331927754367
-1.548639224836185 646  3.12  1.47  -.38   .77   .12 1  5.958017535781948
-1.181443691265908 647  2.81  -.44   -.2  -.57   .07 1  1.758179600545098
-.0883624242566112 648 -3.32   .56 -1.88  -4.5 -1.42 1 -3.509387301462624
 1.654120853093416 649  4.65   .16  -.49  -.49   -.4 1  2.497577726394331
 .7675383150229624 650   .43 -1.23   4.6  1.76  1.91 1  4.418488481502361
 .7337002959205572 651  -.19 -4.21  1.62  2.85  1.09 1 -5.144931450627418
-1.300090055657543 652  2.06 -1.83  -.38   .45 -1.09 1 -.1524946883270472
 3.226876440418037 653  2.61  3.04   -.6  -1.9  -1.9 1  5.056250663074426
-1.639863325627885 654 -2.04 -4.16   .04  1.48   .44 1 -3.606526297369947
-1.073779734353744 655  4.23    .3  -.76  -.91  -.65 1  2.334665259623833
-.7707598557843998 656 -1.97  -3.8 -1.68  1.28  -.62 1 -2.045957417917509
 .1447530739194685 657  2.52  3.79 -1.81  -.78  -.18 1  3.921821214508064
 .1178356353986567 658  2.55 -2.27 -3.37  1.69   .15 1  .2556962153253985
 2.635474229565091 659  -.06  2.85  1.56 -1.52   .81 1  1.699910054766814
-3.078890081227631 660 -3.11  -.91 -3.06  1.09 -1.67 1 -9.063475876310159
 .4817352432663817 661  6.13   .35 -2.16   .06 -1.62 1  7.821358748819074
 .2211978151041967 662 -1.12  3.07  -.73   .16  -.54 1  1.178161237891585
 1.991202305673274 663   .59 -2.99  2.13   .41  -.49 1  1.169923120498791
-1.329517653461937 664  1.36   .85  -1.9 -1.54  -.68 1  2.282959490396854
   .01391606423699 665 -1.53  2.88 -1.04  1.03 -1.51 1  4.042932941219418
 .3699056815049282 666  1.54  -4.5 -4.49   .31  -2.6 1 -.4668957188768061
-.1181539396678257 667 -6.04   .38  2.88   .75  1.14 1 -5.438906417704657
 .1582819462867526 668 -3.07 -2.81   .73  1.66   -.5 1  -1.03380138198852
 1.965128759761292 669  7.75 -2.05  -.32  1.19   .45 1  4.103701862358857
-2.397516434496359 670   .56  3.35 -1.23 -2.11    -1 1  4.884271693531828
-2.359402434240175 671 -2.17    -3 -2.07   .45   .17 1 -5.998214832502895
 1.646352401779502 672 -5.77 -3.56  3.13  2.27     3 1 -9.570953046717952
-.8583285517720807 673  -.07   .87  -.03  2.44  2.09 1 -3.224477640041395
 1.201286271998179 674  6.96  1.01   1.3   .58   .07 1  7.358411454974729
-2.428878818318338 601   3.4  1.53  2.74  -.55  1.43 2  -14.9038461538462
 4.750675543873377 602  6.31  1.85  2.01   -.9  1.67 2  8.772742681047772
-1.547824874555269 603     2  5.03  3.12   .49  1.69 2  5.619839471199241
-2.377775945715615 604 -7.89  -.08 -2.32  1.38  -.18 2 -12.43110815111477
 2.033946504409595 605 -5.56 -2.59 -4.27  -.34 -1.48 2 -6.662716462092019
 1.866966321771243 606  6.93   .13   .04   .32  2.03 2 -18.01929469655549
-6.230064946559605 607 -4.77 -3.07 -1.51   .34 -2.13 2 -13.03805396069644
 5.650530448089943 608  9.54  3.71 -2.94  -.01   .39 2  12.93086977856836
  .453532778924937 609  3.88   .72 -2.23  1.46  -.16 2 -6.991575267138257
-2.051943886966359 610    .6  3.54  -.58   -.1  1.76 2 -2.587763505524829
 2.453635974122106 611  6.82  1.03  3.47 -3.44  3.44 2    25.826417302189
-2.555251514231911 612  1.99 -2.38   .68 -1.07    .8 2 -1.953753199899981
-.3940574407002404 613  3.49  1.76  1.73 -1.76   .72 2  1.908535113442489
 .9019179029163964 614   .45  2.66 -1.16  1.21  -.03 2 -2.394001810935897
-3.366182863817053 615   2.9  -.41 -2.15   .96 -1.28 2 -12.29777807612272
 3.801343710610448 616 -1.27  -.69 -2.12  2.02 -1.46 2 -5.240232404411508
 .5375060083539553 617 -1.75   .09  -.26  2.16  -1.4 2 -3.270575104270688
-.9872492784416207 618 -2.36 -1.38 -1.18  2.41 -1.75 2  9.105746602899874
 .6191782687064706 619 -5.99 -3.39 -1.58  2.79  -.23 2 -16.62784658201427
  .263701731990106 620 -7.59  -3.9  -.98  1.71   .24 2 -22.14350681319415
-1.255992779152897 621 11.35  3.72  -.96 -1.42  -.86 2  11.27604389951129
-7.328765598425764 622  -.28  -.34  -.18  1.46  1.52 2  .3072477726944403
 6.918695219020471 623   .74  -.36  1.57   .59  2.44 2  12.55089107389768
 1.664472961319299 624  5.05  2.35 -2.14 -1.05 -1.41 2   1.90504628247238
-4.488823618117669 625  4.42 -1.54   .01  -.17  -.03 2  5.432489451476787
 2.808398174936492 626  3.11   -.3  -.06   .25   .77 2   13.8513701295092
end
format %tm monthly_date

thanks

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