Dear all,

I have a monthly panel data of firm (variable: gvkey) and their turnover (variable: trn) as follow:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long gvkey float(modate trn) byte(ffind48 tic_newind) float(event_time mean_trn t treated)
1004 360 .073733665 41 0  .         . . .
1004 361  .04401369 41 0  .         . . .
1004 362   .1571562 41 0  .         . . .
1004 363  .13565476 41 0  .         . . .
1004 364   .1139597 41 0  .         . . .
1004 365  .11191394 41 0  .         . . .
1004 366  .04519458 41 0  .         . . .
1004 367  .06838244 41 0 -5 .13748688 . 0
1004 368  .05052841 41 0 -4 .13370831 . 0
1004 369   .1172303 41 0 -3 .15079726 . 0
1004 370  .11515839 41 0 -2 .13852969 . 0
1004 371   .2591975 41 0 -1 .14454024 . 0
1004 372  .13647348 41 0  0  .1533158 0 0
1004 373   .1496822 41 0  1 .13952337 . 0
1004 374  .11926877 41 0  2  .1593396 . 0
1004 375   .0952426 41 0  3 .14585368 . 0
1004 376  .06534516 41 0  4 .14730963 . 0
1004 377  .07027248 41 0  .         . . .
1004 378  .07071927 41 0  .         . . .
1004 379  .06913976 41 0 -5 .13748688 . 0
1004 380  .05471021 41 0 -4 .13370831 . 0
1004 381  .07118324 41 0 -3 .15079726 . 0
1004 382  .06878027 41 0 -2 .13852969 . 0
1004 383  .10182425 41 0 -1 .14454024 . 0
1004 384  .08385859 41 0  0  .1533158 0 0
1004 385 .064968236 41 0  1 .13952337 . 0
1004 386  .03993835 41 0  2  .1593396 . 0
1004 387  .03755582 41 0  3 .14585368 . 0
1004 388   .0423926 41 0  4 .14730963 . 0
1004 389  .06519907 41 0  .         . . .
1004 390  .04824517 41 0  .         . . .
1004 391  .03668729 41 0 -5 .13748688 . 0
1004 392  .03870092 41 0 -4 .13370831 . 0
1004 393  .07271002 41 0 -3 .15079726 . 0
1004 394  .03570977 41 0 -2 .13852969 . 0
1004 395  .09181427 41 0 -1 .14454024 . 0
1004 396  .06065543 41 0  0  .1533158 0 0
1004 397  .06803372 41 0  1 .13952337 . 0
1004 398  .08417411 41 0  2  .1593396 . 0
1004 399  .03986542 41 0  3 .14585368 . 0
1004 400  .04606628 41 0  4 .14730963 . 0
1004 401  .04063267 41 0  .         . . .
1004 402 .028142884 41 0  .         . . .
1004 403  .02075341 41 0 -5 .13748688 . 0
1004 404  .02452676 41 0 -4 .13370831 . 0
1004 405  .05119422 41 0 -3 .15079726 . 0
1004 406 .022928976 41 0 -2 .13852969 . 0
1004 407 .026178503 41 0 -1 .14454024 . 0
1004 408   .0652122 41 0  0  .1533158 0 0
1004 409  .05849104 41 0  1 .13952337 . 0
1004 410  .04123861 41 0  2  .1593396 . 0
1004 411  .01479316 41 0  3 .14585368 . 0
1004 412 .018326418 41 0  4 .14730963 . 0
1004 413 .026970955 41 0  .         . . .
1004 414  .02297102 41 0  .         . . .
1004 415 .019287106 41 0 -5 .13748688 . 0
1004 416  .04034073 41 0 -4 .13370831 . 0
1004 417  .04149912 41 0 -3 .15079726 . 0
1004 418   .0207527 41 0 -2 .13852969 . 0
1004 419  .03840789 41 0 -1 .14454024 . 0
1004 420 .024877865 41 0  0  .1533158 0 0
1004 421 .016278341 41 0  1 .13952337 . 0
1004 422  .02945008 41 0  2  .1593396 . 0
1004 423  .05513439 41 0  3 .14585368 . 0
1004 424 .029302675 41 0  4 .14730963 . 0
1004 425  .04256625 41 0  .         . . .
1004 426  .04514694 41 0  .         . . .
1004 427  .02081584 41 0 -5 .13748688 . 0
1004 428  .02141738 41 0 -4 .13370831 . 0
1004 429 .012623002 41 0 -3 .15079726 . 0
1004 430 .020150423 41 0 -2 .13852969 . 0
1004 431  .03882795 41 0 -1 .14454024 . 0
1004 432  .05951326 41 0  0  .1533158 0 0
1004 433  .05526989 41 0  1 .13952337 . 0
1004 434  .05905772 41 0  2  .1593396 . 0
1004 435  .03972372 41 0  3 .14585368 . 0
1004 436 .063389175 41 0  4 .14730963 . 0
1004 437  .04341168 41 0  .         . . .
1004 438  .10945938 41 0  .         . . .
1004 439   .0552653 41 0 -5 .13748688 . 0
1004 440  .13748042 41 0 -4 .13370831 . 0
1004 441  .12149898 41 0 -3 .15079726 . 0
1004 442  .10068192 41 0 -2 .13852969 . 0
1004 443  .08416093 41 0 -1 .14454024 . 0
1004 444  .09832011 41 0  0  .1533158 0 0
1004 445   .1345781 41 0  1 .13952337 . 0
1004 446  .14719102 41 0  2  .1593396 . 0
1004 447 .067913644 41 0  3 .14585368 . 0
1004 448  .06246979 41 0  4 .14730963 . 0
1004 449  .13060866 41 0  .         . . .
1004 450   .0830844 41 0  .         . . .
1004 451  .08758722 41 0 -5 .13748688 . 0
1004 452   .0764337 41 0 -4 .13370831 . 0
1004 453  .08064376 41 0 -3 .15079726 . 0
1004 454  .04825445 41 0 -2 .13852969 . 0
1004 455  .06884157 41 0 -1 .14454024 . 0
1004 456  .07597327 41 0  0  .1533158 0 0
1004 457  .06794872 41 0  1 .13952337 . 0
1004 458  .09447815 41 0  2  .1593396 . 0
1004 459  .05477548 41 0  3 .14585368 . 0
end
format %tm modate
My main objective is whether there a significant difference in the turnover (trn) when a firm join a new industry.
To do so, I need a control group. The control group is composed by those firm that, in the moment when a firm of the same industry (I classified industry with ffind48 variable) jumps in a different one, they remain in the same industry sector. So no industry changes for them.
To be more clear: 2 firms belong to industry 1 in month 1. In month 2, one firm goes in industry 2, but the other will stay in 1.

I guess a solution is a propensity score matching based on the size (I have a variable that the name is "at"- assets total and "mkt" - market capitalization"). Nevertheless, the match should have a constraint that the firm must belong to the same industry before the event. So the matched should consider the same industry sector.

I created a treatment variable "t". If it is 1, it means that the firm belongs to a different industry compared to previous month. Is 0 for those firm that didn't change the industry. This variable can be either 1 or 0 only in january, because I don't have changes in other months. So, other months will be missing value.

Finally, as you can see from the data I also created some other variables such as event_time, that track the months before and after the event study.

If you would be so kind to give me some suggestion, I really appreciate.
Thanks in advance for your time!