Placebo test

[ALKEBSEE RADWAN]

Hello every one

I am addressing a problem with the process of Placebo test

First, I am using the following process

set seed 12345

gen randomplacebo = runiform()
bysort code (randomplacebo): keep if _n == 1 
gen placebo_gb = 0 
bysort code: gen rank = _n 
bysort code: replace placebo_gb = 1 if rank <= 167 


gen random_year = floor(2016 + 7 * runiform())
replace random_year = . 
replace random_year = floor(2016 + 7 * runiform()) if placebo_gb == 1

gen PlacPost = (year >= random_year & placebo_gb == 1) 

matrix plac_coeff = J(1000, 1, .)

forval i = 1/1000 {
    quietly reg  SL PlacPost growth Bsize dual bmeet bindp size leverage mtb roa own soe , robust
    matrix plac_coeff[`i', 1] = _b[PlacPost] 
}

the problem represent in two issues
1- the matrix command does not create the plac_coeff
I don't know why
thenI tried to fix it all of a sudden the second issue appeared which is getting an error message (reg is unrecognized command)

Below id the data example

---------------------- copy starting from the next line -----------------------
[CODE]
* Example generated by -dataex-. For more info, type help dataex
clear
input double SL byte gb double growth float Bsize byte(dual bmeet bindp) double(size leverage mtb roa Institutionalownership) byte tateowned1no0
                   . 0     .049910983979371 11 0 16 4 29.351484298706055  .9154999852180481  30.661100387573242   .008500000461935997             .67149 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .09834200143814087 0      .25523611968287  9 0  5 3 19.305076599121094  .4032000005245209   .2475000023841858   .026599999517202377            .356486 0
 .053509000688791275 0  -.19098814131373998 14 0  9 5 21.620494842529297  .4593999981880188   .9158999919891357   -.14079999923706055 .19131399999999998 0
 .021412000060081482 0   .16732135609520998  9 0 14 3 23.187393188476563  .6413999795913696  1.2348999977111816   .047200001776218414            .370434 1
  .07607199996709824 0     .035608975975038  6 0  9 3  20.33896255493164 .47429999709129334  .20190000534057617   -.04989999905228615            .280746 0
  .11843899637460709 0     .020846702148776  7 0  7 3 20.255861282348633  .1005999967455864  .22930000722408295    .03669999912381172            .304193 1
 .022972000762820244 0      .10108411233325  9 1 12 3 24.119443893432617  .6499000191688538  3.2221999168395996   .023399999365210533 .20340499999999997 0
  .06369999796152115 0  .054143947990315994 10 0 18 4   21.9090576171875  .8195000290870667  1.2246999740600586   -.22779999673366547            .356523 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .015778999775648117 0      .36626875061248  7 0 15 3 23.882762908935547  .6520000100135803  1.9980000257492065     .2493000030517578 .34084200000000003 1
 .030642999336123466 0     -.10915888625072  8 1  6 3  17.64130210876465  .7081000208854675 .008200000040233135  -.002199999988079071            .211736 0
 .011579000391066074 0      .46100472358042  9 0 12 3 23.180103302001953  .8062999844551086   1.222499966621399    .03909999877214432             .24984 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .07369200140237808 0    -.040811754553639  7 1 10 3 20.402915954589844  .6327999830245972   .3495999872684479   .012400000356137753                  0 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .030626999214291573 .                    .  . .  . .                  .                  .                   .                     .                  . .
  .03307599946856499 0     .062322601922208  9 0 16 3  21.17170524597168  .7148000001907349  .36959999799728394   .017500000074505806            .448203 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .01860399916768074 0   .18169905564574002  8 0 16 3 21.229055404663086 .33709999918937683  .22579999268054962   .056299999356269836  .7832389999999999 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .014062000438570976 0                    .  9 0 11 3  24.13275718688965  .4814999997615814  1.0128999948501587    .07769999653100967                  . 1
  .01681699976325035 0 -.003924904188825701 10 0  9 4  24.47549057006836  .5436999797821045  3.2351999282836914    .04149999842047691            .735984 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .03072500042617321 0        .119258384875  9 0 14 3  23.39475440979004  .4277999997138977  1.1057000160217285    .06639999896287918            .801096 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .17437300086021423 0   .08323242724564199  7 0  5 3 20.204423904418945 3.0195000171661377    .593999981880188    .08169999718666077              .1133 0
 .011056000366806984 0      .50462039954484 11 0  8 4 22.998014450073242  .5861999988555908  1.0130000114440918     .1671999990940094            .355596 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
   .0335410013794899 0    -.038441168975833  6 1 18 3  21.79387855529785  .0560000017285347   .4708999991416931   .041600000113248825            .354792 0
 .013434999622404575 0      .21652860506308  8 0 26 3 25.791440963745117   .670199990272522   6.424699783325195    .02810000069439411            .490576 0
  .11671900004148483 0  -.17310673797027998 11 0 13 4 21.358858108520508  .4779999852180481  1.1654000282287598     .0674000009894371 .36304499999999995 0
  .06391999870538712 0     -.11464818125397  8 1  4 3  24.32569122314453  .7760999798774719   6.949900150299072 .00019999999494757503  .7138549999999999 0
 .022823000326752663 .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .02340400032699108 0   .11880087320103999 18 0 17 6  25.95850944519043   .853600025177002   4.844099998474121   .017400000244379044            .808165 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .05964000150561333 0    -.021250012970181 11 0 13 4  21.38636589050293  .5866000056266785   .1363999992609024    .10689999908208847            .433977 0
 .024584999307990074 0      .29886796731402  9 0  5 3 20.561124801635742  .6726999878883362   .2362000048160553    .11649999767541885            .496198 0
  .01816900074481964 0      .51211310174228  9 0 12 3   22.7997989654541   .588100016117096  1.2303999662399292    .03620000183582306            .566122 1
 .028418999165296555 0     .039461170949946  7 1  6 3  23.26841926574707  .5432999730110168   3.208699941635132    .02290000021457672            .211519 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .03984300047159195 0    -.034060130126626  9 0 12 3 22.451797485351563  .5430999994277954   .7978000044822693    .01720000058412552            .464224 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .011703000403940678 0      .12940504787279 13 0  7 5 23.348308563232422   .554099977016449   .6209999918937683   .010400000028312206            .693481 1
   .0807889997959137 0      .52796498371664  9 1 30 3 22.544965744018555  .4049000144004822  .23420000076293945     .0731000006198883  .7762720000000001 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
   .0571569986641407 0     -.16477859241217  6 0  5 3 21.392717361450195  .7064999938011169   .6272000074386597    .02319999970495701  .6247590000000001 1
.0024399999529123306 0      .18817371391192  9 0 39 3 24.210969924926758  .6710000038146973   .7857000231742859   .022700000554323196 .46820799999999996 1
   .0398470014333725 0                    .  9 0 11 3  20.36865997314453  .7486000061035156  .09749999642372131    -.9986000061035156                  . 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .014310999773442745 0      .42255819614562  8 0 20 3 23.034378051757813  .7049999833106995  1.3410999774932861   .006800000090152025 .45431600000000005 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .015792999416589737 0  .043902551988715004  9 0  8 3  23.24738311767578 .14100000262260437    .760699987411499     .0471000000834465            .700849 1
  .03773999959230423 0                    .  7 0 12 3 22.496482849121094   .552299976348877   1.079300045967102   .010200000368058681                  . 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .015852000564336777 0      .14867623157033 15 0 11 5 22.217166900634766   .388700008392334    .534500002861023    .04960000142455101             .42896 1
.0032279998995363712 0   .08942154003554201 12 1 13 4 25.800275802612305  .6621999740600586   3.041100025177002   .023099999874830246 .29118099999999997 0
  .06755799800157547 0     .083699528470525  7 0 20 3  22.83640480041504  .6952000260353088   .8960000276565552   .025299999862909317            .465391 0
  .08585699647665024 0      .49467501404662  9 0 11 3 21.635513305664063  .6067000031471252   1.152400016784668    .04450000077486038            .454894 1
  .06205400079488754 0     .042826530949328  9 0 10 3 21.558467864990234   .486299991607666   .7074000239372253   .013899999670684338             .29948 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .12762600183486938 0      .12522447336726 13 1 15 4 21.942197799682617  .7487000226974487   .2957000136375427    .09470000118017197            .023908 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .024248000234365463 0   -.0049410912839277  9 0  0 3 21.811241149902344  .2644999921321869   .6065999865531921   .044599998742341995             .32241 0
.0025740000419318676 0      .22374620226563 11 0 11 4  27.12187957763672  .8219000101089478   5.038899898529053   .017500000074505806             .76265 1
 .004184000194072723 0      .58460762144208  7 1 21 3 25.606090545654297  .7771999835968018  1.4117000102996826    .05220000073313713  .6143339999999999 0
 .004726000130176544 0      .10496765836308 14 1  9 5 25.297420501708984  .5968999862670898  1.1497999429702759    .08990000188350677            .500071 0
.0030980000738054514 0      .15960978622249 14 1  8 5 25.968381881713867  .7027999758720398  2.9449000358581543   .051899999380111694             .30975 1
 .022926999256014824 0    -.047803615854213  9 0 11 3   23.4027042388916  .4269999861717224  1.6261999607086182   .018300000578165054            .550543 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .005884999874979258 0      .27893578476124  9 0 17 3  24.71535873413086  .6816999912261963   2.707200050354004    .03799999877810478 .41822899999999996 1
  .02875399962067604 0      .11162548692244  9 1  7 3 20.894826889038086  .7038000226020813  .27709999680519104     .0568000003695488            .034698 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
   .3730219900608063 0     -.20860610039283  8 0 13 3 19.938995361328125  .6985999941825867   .1712000072002411   -.10729999840259552 .21473199999999998 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .027778999879956245 0                    .  9 0 11 3  22.91562271118164  .8416000008583069  1.4505000114440918   .005799999926239252                  . 1
  .12467099726200104 0   .16724316933720002  9 0  5 3 22.291383743286133  .7404000163078308   2.175600051879883    .03790000081062317            .637891 1
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
 .026197999715805054 0 -.007918100740661101  9 0  9 3 26.237625122070313  .8392999768257141  13.792900085449219 -.0044999998062849045            .521751 0
                   . .                    .  . .  . .                  .                  .                   .                     .                  . .
  .04418500140309334 0     .028251837408894  9 0  5 4  23.13256072998047  .5645999908447266  2.9598000049591064    .01720000058412552  .5731430000000001 1
  .06007400155067444 0  -.08107873104593401  9 0  7 3 22.851938247680664  .4941999912261963  1.5194000005722046    .04320000112056732            .459741 0
 .024880999699234962 0     .016766102680143  9 1 12 3 22.069517135620117  .4869999885559082   .7854999899864197   .029100000858306885 .49847299999999994 1
   .1817139983177185 0       .1229802919175 11 0 14 4 21.961795806884766  .8253999948501587   2.895900011062622  .0026000000070780516            .086064 1
end
[/CODE]
---------

[George Ford]

it's probably not estimating the coefficient due to a coding error.

ritest will do this for you.

what exactly are you trying to do? what is the nature of the data? is this DID?

[Clyde Schechter]

Like George Ford, I do not understand exactly what is being attempted with this code. I'll just comment on a few items that catch my attention.
[quote]getting an error message (reg is unrecognized command)[quote]

The most common cause of this kind of message when the command, -reg- in this case, is clearly a valid Stata command, is that the code in question has been copy/pasted from a source that may include non-printing formatting characters. Examples of such sources include Microsoft Word documents, PDF files, and websites (including, yes, Statalist). These non-printing characters do not show up in the Results window or do-file, but they are there and Stata's parser "sees" them. So it is likely that somewhere in what looks to our eyes like -reg-, there is embedded one or more of these invisible characters. While there are some ways to have Stata identify them and remove them from the code, they are somewhat complicated. It is usually simpler to resolve this kind of problem by simply deleting that line of code from the program and retyping it by hand. (Do not repeat the copy/paste operation--it will give you the same problem every time.) Be careful to avoid introducing typos.

gen random_year = floor(2016 + 7 * runiform())
replace random_year = .
replace random_year = floor(2016 + 7 * runiform()) if placebo_gb == 1

This sequence of code, though legal, and probably not the cause of the problems reported by O.P., is awkward, unnecessarily complicated, and can be easily improved:

gen random_year = runiformint(2016, 2022) if placebo_gb == 1

Finally I'll point out that the example data shown is of no use for troubleshooting the questions posed because it does not include the variables code, and year, which appear in many of the commands. (It may be missing other variables as well--I stopped checking after finding out about those two.)

[ALKEBSEE RADWAN]

it's probably not estimating the coefficient due to a coding error.

ritest will do this for you.

what exactly are you trying to do? what is the nature of the data? is this DID?

First of all thank you very much for responding
Secondly, this is not a DID I am trying to perform placebo test I just typed POST randomly
my dependent variable id stock return and the IV is green innovation. I am trying here to apply placebo as test for addressing endogeneity concern (some call it for robustness)

[ALKEBSEE RADWAN]

[QUOTE=Clyde Schechter;n1769932]Like George Ford, I do not understand exactly what is being attempted with this code. I'll just comment on a few items that catch my attention.
[quote]getting an error message (reg is unrecognized command)

The most common cause of this kind of message when the command, -reg- in this case, is clearly a valid Stata command, is that the code in question has been copy/pasted from a source that may include non-printing formatting characters. Examples of such sources include Microsoft Word documents, PDF files, and websites (including, yes, Statalist). These non-printing characters do not show up in the Results window or do-file, but they are there and Stata's parser "sees" them. So it is likely that somewhere in what looks to our eyes like -reg-, there is embedded one or more of these invisible characters. While there are some ways to have Stata identify them and remove them from the code, they are somewhat complicated. It is usually simpler to resolve this kind of problem by simply deleting that line of code from the program and retyping it by hand. (Do not repeat the copy/paste operation--it will give you the same problem every time.) Be careful to avoid introducing typos.

gen random_year = floor(2016 + 7 * runiform())
replace random_year = .
replace random_year = floor(2016 + 7 * runiform()) if placebo_gb == 1

This sequence of code, though legal, and probably not the cause of the problems reported by O.P., is awkward, unnecessarily complicated, and can be easily improved:

gen random_year = runiformint(2016, 2022) if placebo_gb == 1

Finally I'll point out that the example data shown is of no use for troubleshooting the questions posed because it does not include the variables code, and year, which appear in many of the commands. (It may be missing other variables as well--I stopped checking after finding out about those two.)

Exactly I copied it from a word file.
But, even when I retyped the command manually still gives same error message ( only with this command)

I am here reposting the data again with code and year

---------------------- copy starting from the next line -----------------------
[CODE]
* Example generated by -dataex-. For more info, type help dataex
clear
input long code int year double SL byte gd double growth float Bsize byte(dual bmeet bindp) double(size leverage mtb roa Institutionalownership) byte tateowned1no0 long sic1
600598 2016  .008914000354707241 0     .070149881532048  9 0 10 4  22.76995849609375  .26159998774528503  .3569999933242798   .09529999643564224            .693406 1 1
  2041 2016  .023329000920057297 0        .039535125972 12 0  9 4 22.220090866088867  .18050000071525574  .2689000070095062   .11919999867677689            .693445 0 1
300511 2016    2.101619005203247 0      .38642983513327  9 1  9 3   21.6236629486084   .4758000075817108  .3391999900341034    .0502999983727932             .01489 0 1
601118 2016  .009176000021398067 0     .045937407312199  8 1 13 3 23.340232849121094   .4018999934196472  .5005000233650208  .004800000227987766            .751737 1 1
   998 2016  .007809999864548445 0      .58105084565988 15 0  9 5 22.795534133911133   .2728999853134155 .29510000348091125   .07739999890327454            .517045 1 1
600108 2016  .008716999553143978 0  .023180536720126002  9 0  9 4 22.776878356933594    .392300009727478  .7001000046730042  .010200000368058681 .33267800000000003 1 1
  2772 2016  .021524999290704727 0      .91034601805157  9 0 14 3 21.890966415405273  .28690001368522644  .4185999929904938   .06589999794960022            .189754 0 1
300189 2016   .01571900025010109 0      .41904139788627  8 0 14 3 21.898082733154297  .35359999537467957  .6021999716758728  .010300000198185444            .109902 0 1
300087 2016  .030520999804139137 0      .29126510249498  9 1 10 4 21.081260681152344  .38830000162124634 .33809998631477356   .04129999876022339            .299058 1 1
600371 2016   .03388400003314018 0   .12000018462305001  9 0  4 3   20.4335994720459  .40310001373291016  .2037000060081482   .07980000227689743            .527791 0 1
600359 2016  .019850000739097595 0     .020898937387623  7 0 12 3 21.753833770751953   .7639999985694885  .7822999954223633  -.06430000066757202             .45572 1 1
600506 2016   .04126900061964989 0 -.037895376708855004  7 0  6 3 19.461305618286133 .054999999701976776 .05490000173449516  -.02019999921321869            .241222 1 1
600313 2016  .022995999082922935 0      .10638569535479  7 1 10 3 22.139551162719727   .3781999945640564  .5770999789237976   .02370000071823597            .460854 1 1
600354 2016  .025172999128699303 0     -.14062336299388 11 0  5 4  21.83226776123047   .5393999814987183  .6304000020027161   -.0982000008225441            .257316 1 1
600540 2016  .031089000403881073 0  -.13390820699230999  8 0  8 3 21.650381088256836   .7888000011444092  .6783999800682068   -.2632000148296356 .43418599999999996 1 1
   592 2016  .008729999884963036 0   .09812774160333801  5 0 11 2  22.15131950378418    .163100004196167 .31940001249313354 .0013000000035390258             .36186 0 2
600265 2016    .3242799937725067 0     -.03959161763618  9 1 15 3 19.612668991088867   .9473000168800354 .08259999752044678   .10320000350475311            .524501 0 2
  2679 2016  .032235998660326004 0    .0099837696076591  9 0  4 3 21.235795974731445   .5582000017166138 .30219998955726624  .015300000086426735            .724847 1 2
  2200 2016   .02704799920320511 0   .19142199341091998  9 0 12 3  21.95781135559082    .710099995136261   .796999990940094  .021400000900030136 .37574599999999997 1 2
   735 2016  .013895000331103802 0       .3735739845119  7 0 11 3 22.545337677001953  .42719998955726624  .7501000165939331  .016699999570846558            .376747 0 3
  2458 2016   .01929599978029728 0       .1038669145384  9 0  6 3 21.384437561035156  .23880000412464142  .1687999963760376   .30329999327659607            .292431 0 3
  2234 2016   .01958799920976162 0   .20432180067566003  9 0  6 3  21.50198745727539   .5270000100135803   .334199994802475    .0771000012755394            .312563 0 3
300106 2016  .029733000323176384 0      .22106668914585  9 0 10 3  21.74315643310547   .6054999828338623  .8134999871253967  -.01850000023841858             .60244 1 3
  2157 2016  .009487000294029713 0      .25558743202964  5 0 19 2 23.229522705078125   .4927000105381012  .9136999845504761   .09430000185966492 .38600900000000005 0 3
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end

I need to perform placebo test. I saw many papers use placebo they mention something like running the regression 1000 times to estimate the coefficient of placebo and compare it with the coefficient of IV from the main regression model.

[Clyde Schechter]

Thank you for the updated data example. It is still missing a couple of variables that appear in the regression, but by omitting them from the regression, it is possible to get the code to run.

It does not produce on my setup with this example data any of the problems you describe in #1. And it does create a non-empty plac_coef matrix. The matrix is not very useful because all of its entries are the same number. This is unsurprising because your loop simply repeats the same regression 1000 times and stores the coefficient in each of the 1000 rows of the matrix. I imagine this is not what you intended to do. Presumably you intended to vary either the regression itself, or perhaps which observations would be in the estimation sample during the loop. Or perhaps the idea was to incorporate placebo_gb and plac_post into the regression in some way, and to get 1000 different results like that would have required putting the code that creates those variables inside the loop, not before it.

Anyway, as I can't tell specifically what you wanted to do, I can't say anything more specific than that.

As for still getting that error message about -reg- not being a valid command, I think you need to start with a New do-file and hand-type in all of the code, not just that one line.

[ALKEBSEE RADWAN]

It does not produce on my setup with this example data any of the problems you describe in #1. And it does create a non-empty plac_coef matrix. The matrix is not very useful because all of its entries are the same number. This is unsurprising because your loop simply repeats the same regression 1000 times and stores the coefficient in each of the 1000 rows of the matrix. I imagine this is not what you intended to do.

Thank you very much for you detailed response.
I do understand that the matrix command is working with you. that is good
but, You mentioned that the 1000 regression would give the same coefficient. actually it is true becuse no new variables added to the model.
Anyway I am just trying to follow some studies with this test.

I want to mitigate the potential influence of unobserved factors on my main results using a placebo analysis by re-estimating my baseline regression based on randomly selected firms.
Hope you could help (DV= SL and IV=gb).

[Clyde Schechter]

I'd like to help, but I still don't have a clear sense of what you want to do. Simply selecting random firms, which is somewhat like what your code does, is not a placebo test. It's actually just a version of bootstrapping. For a placebo test you replace the IV with a variable that is randomized. Or, if you are dealing with a DID scenario, replacing the time of onset with a randomly selected time. There are other variants of placebo testing as well, where the IV and time variables are modified not at random but systematically in ways that target particular forms of confounding.
On top of that, your example data does not lend itself to manipulations of the time variable because all observations have year == 2016.

Added: Perhaps this will help. Below is an toy example using the Stata website's grunfeld data set. I first test an "intervention" that affects companies 1 through 5 starting in year 1942. I then do 25 randomized placebo tests, collecting the results in a frame. Maybe you can use this as a model and adapt it to your own situation.

clear*
webuse grunfeld

//    ANALYZE A "TREATMENT" OCCURRING IN 1942 APPLIED TO COMPANIES 1-5
gen byte treatment = (company <= 5)
gen byte post = (year > 1942)
regress mvalue i.treatment##i.post

//    NOW DO 25 "PLACEBO" RUNS
set seed 1234
summ year, meanonly
local first_year `r(min)'
local last_year = r(max) - 1

summ company, meanonly
local first_company `r(min)'
local last_company `r(max)'
gen double shuffle = .

frame create results int run_um float(coef se)
forvalues i = 1/25 {
    local placebo_year = runiformint(`first_year', `last_year')
    replace post = (year > `last_year')
    
    sort company year
    by company: replace shuffle = runiform() if _n == 1
    by company: replace shuffle = shuffle[1]
    sort shuffle year
    replace treatment = _n <= _N/2
    regress mvalue i.treatment##i.post
    frame post results (`i') (_b[1.treatment#1.post]) (_se[1.treatment#1.post])
}

frame results: list, noobs clean

Again, to be clear, I would not ordinarily do a placebo test with this kind of complete randomization of treatment group and time of intervention. If I were concerned about collateral effects of other influences active in 1942, I would keep the original treatment group and see what happens with random placebo years. (Actually, I probably wouldn't randomize year either; I'd try each year once.) Or if I were concerned that other factors common to the original treatment group were a concern, I'd probably keep the year fixed at 1942 but try random subsets of companies as the placebo treatment groups. The code above randomizes both treatment group an year: I did this primarily to show you how one could code both of those randomizations, as it isn't clear to me which one you really need, or if, peculiarly, you really do need to randomize both.