I want to apply a difference in difference estimations to observe differences in the dependent variable for firms that appoint a female director(s) for the first time and those who did not appoint any female directors while controlling for a set of variables (i have put size as a sample here) and year and industry dummies. i am interested in creating treated and control groups wherein treated are firms who hired the female director for the first time and control firms are similar firms based on control variables who did not hire any female director (all male firm). Based on this psmatch sample i want to create two dummies of post and treatment wherein post is equal to 1 for each firm in three years following the event of hiring a female and zero for three years pre-event (we exclude the event year) and treatment is equal to 1 for three years pre and three years post hiring for all the firms who hired the female for the first time and zero otherwise (again with a window of three year pre and post the event while excluding the year of the event).


I am attaching below the sample data and the sequence I am following.
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long gvkey double year int sic3 float(size_w aray_w total_females all_male total_board)
1004 1996 508  6.272092  .23215203 0 1  0
1004 1997 508  6.508111  .24361615 0 1  5
1004 1998 508  6.588418   .2261151 0 1  5
1004 1999 508  6.607998  .17320964 0 1  5
1004 2000 508  6.553725   .1641182 0 1  5
1004 2001 508  6.565545  .10916377 0 1  7
1004 2002 508  6.531783  .09659186 0 1  8
1004 2003 508  6.564267    .147557 0 1  7
1004 2004 508  6.596095    .173608 0 1  8
1004 2005 508  6.886347  .13922083 0 1  7
1004 2006 508  6.973199  .17018114 0 1  9
1004 2007 508  7.216717  .14865677 0 1 11
1004 2008 508  7.228034  .16500776 0 1 11
1004 2009 508  7.313915  .15886697 0 1 11
1004 2010 508  7.440574  .17402378 0 1 10
1004 2011 508  7.694235  .14796464 0 1 11
1004 2012 508  7.667111  .14759699 0 1 11
1004 2013 508  7.695985  .13543987 0 1 11
1004 2014 508  7.323171  .15254125 0 1 11
1004 2015 508  7.273856  .16829623 0 1 11
1004 2017 508  7.329553  .13314095 1 0 12
1004 2018 508  7.324622    .170116 1 0 12
1013 1996 366  6.644785  .21231325 0 1  0
1013 1997 366  6.841939  .26299286 1 0  6
1013 1998 366  7.170571  .28140295 1 0  6
1013 1999 366  7.422092   .2611321 1 0  6
1013 2000 366  8.286647   .1799018 1 0  7
1013 2001 366  7.823926  .11229347 1 0  7
1013 2002 366  7.042461   .1231428 1 0  8
1013 2003 366  7.167732    .108875 1 0  9
1013 2004 366    7.2641  .13619494 1 0 10
1013 2005 366  7.336286  .15224756 2 0 13
1013 2006 366  7.384859  .11983369 1 0 12
1013 2008 366  7.560601  .12524727 1 0 10
1013 2009 366  7.203108   .1490771 1 0 10
1034 1997 283  6.448678   .2020001 0 1  3
1034 1998 283  6.812274   .1867502 0 1  3
1034 1999 283  7.056405  .16311863 0 1  3
1034 2000 283   7.38426  .15122187 1 0  4
1034 2001 283  7.779052  .10847077 1 0  4
1034 2002 283  7.739326  .10201775 1 0  5
1034 2003 283  7.753309  .11096662 3 0  8
1034 2004 283  7.602822  .11307827 3 0  9
1034 2005 283  7.392268  .05599295 3 0 10
1034 2006 283  6.832211  .11630982 1 0  6
1034 2007 283  7.160974  .10110972 0 1  6
1045 2015 451 10.787565 .029433027 1 0 11
1045 2016 451  10.84494  .03108788 2 0 13
1045 2017 451 10.847316 .034088258 2 0 13
1045 2018 451  11.01172  .02816111 2 0 12
1045 2019 451 11.002016   .0291691 2 0 10
1055 1996 357  6.722698  .48138815 0 1  0
1056 1996 382 4.7575736   .2989072 0 1  0
1056 2002 382  5.763513   .2132699 0 1  8
1056 2003 382  5.800958   .1973377 0 1 10
1056 2004 382  6.312444  .17597495 0 1 11
1056 2005 382  6.379867   .1717677 0 1  7
1056 2006 382  6.458373  .18854523 0 1  9
1072 1998 367  6.964173  .15128823 0 1  1
1072 1999 367  7.176507  .19481233 0 1  1
1072 2000 367  7.541735  .16252047 0 1  1
1072 2001 367   7.43343  .07638513 0 1  1
1075 1996 491  8.852134 .032150213 0 1  0
1075 1997 491  8.832065  .03533595 2 0  6
1075 1998 491  8.828281  .04419576 2 0  6
1075 1999 491  8.796113  .04804686 2 0  8
1075 2000 491  8.874749   .0823018 3 0 10
1075 2001 491  8.984913  .05554823 3 0 11
1075 2002 491  9.039055  .05315931 3 0 12
1075 2003 491  9.162869  .04667789 3 0 12
1075 2004 491  9.199962  .04609535 3 0 12
1075 2005 491   9.33456  .04395634 3 0 12
1075 2006 491  9.346264  .04329901 3 0 12
1075 2007 491  9.327564  .04013132 2 0 11
1075 2008 491  9.360491 .033509888 3 0 12
1075 2009 491  9.376546 .034445092 3 0 13
1075 2010 491   9.42244 .033934165 3 0 11
1075 2011 491  9.481208 .030941457 3 0 12
1075 2012 491  9.501488  .02755909 2 0 10
1075 2013 491  9.511088 .029129185 2 0 10
1075 2014 491   9.56896  .02760877 2 0 11
1075 2015 491  9.617687 .024474293 1 0 10
1075 2016 491   9.68061 .022206785 1 0 10
1075 2017 491   9.74209  .02438839 2 0 11
1075 2018 491  9.779295   .0227006 2 0 10
1075 2019 491  9.824404 .020911025 2 0 11
1076 2001 735   5.98443 .063975975 1 0  8
1076 2002 735  6.181357   .0557699 1 0  8
1076 2003 735  6.319494  .05560678 1 0  9
1076 2004 735  6.551492  .04674648 1 0 10
1076 2005 735  6.755204   .0498675 1 0 10
1076 2006 735   6.88715  .04440051 0 1 10
1076 2010 735  7.314601  .04637727 0 1 11
1076 2011 735  7.458848  .05041123 0 1 10
1076 2012 735  7.502699  .05469437 1 0  9
1076 2013 735  7.510527  .03759025 2 0 10
1076 2014 735  7.806633  .09421762 2 0  9
1076 2015 735  7.885658  .14231884 2 0 10
1076 2016 735  7.869301  .07357968 2 0 10
1076 2017 735  7.898138  .10731563 2 0  9
end



Code:
*hired female first time

bys gvkey: gen hired_female=1 if total_females>0 & total_females[_n-1]==0 /// there are missing years in the data
replace hired_female=0 if hired_female==. & all_male==1 /// I need to match firms that hired females with those that did not hire females hence I need to keep only those who hired females for the first time and match them with all male firms

tsset gvkey year
*to keep only first female hiring event per firm
tsspell hired_female if hired_female==1
summarize _spell, detail
forvalues x = 1/`=r(max)'{
    gen hired_female_`x' = (_spell == `x')
}  /// there are missing years so the code gave me a warning. Anyways, the purpose was to only keep the first female hiring event per firm and that was fulfilled i suppose. 


drop _*

psmatch2 hired_female_1 size_w i.year i.sic3 , out(aray_w) neighbor(1) noreplacement caliper(0.01)

*generating treatment
bys gvkey: egen treatment=max( hired_female_1) if _weight==1
*generating post
by gvkey (year): gen byte post = sum( hired_female_1 ) if _weight==1
*alternate generating treatment
by gvkey (year): egen byte treatment1= max(post) if _weight==1

*three years window
by gvkey (year): gen post1 = inlist(1, hired_female_1 , L1.hired_female_1 , /// 
F1.hired_female_1) if _weight==1 /// I have doubts about this code. this did not generate what I wanted
* i need to run a three-year window for the treatment group as well but i don't know how to do it. 



*alternatively, I did this to generate post and treatment observations after psmatch2 command

gen control=1 if _weight==1 & _treated==0
gen treated=1 if _weight==1 & _treated==1

*control group
bysort gvkey: egen before_after_control = min(cond(control== 1, year, .)) 

gen before_after_dummy_control = before_after_control == year

forvalues i = 1/3 {
    gen before_after_dummy_cminus`i' = year == (before_after_control - `i') 
}
forvalues i = 1/3 {
    gen before_after_cplus`i' = year == (before_after_control + `i')
}

*treatment group
bysort gvkey: egen before_after_treated = min(cond(treated== 1, year, .)) 
gen before_after_dummy_treated = before_after_treated == year

forvalues i = 1/3 {
    gen before_after_dummy_minus`i' = year == (before_after_treated - `i') 
}
forvalues i = 1/3 {
    gen before_after_plus`i' = year == (before_after_treated + `i') 
}

gen treatment=1 if before_after_dummy_minus1==1 | before_after_dummy_minus2==1| before_after_dummy_minus3==1 | before_after_plus1==1 | before_after_plus2==1 | before_after_plus3==1
replace treatment=0 if before_after_dummy_cminus1==1 | before_after_dummy_cminus2==1 | before_after_dummy_cminus3==1 | before_after_cplus1==1 | before_after_cplus2==1| before_after_cplus3==1
tab treatment
gen post=1 if before_after_cplus1==1 | before_after_cplus2==1 | before_after_cplus3==1 | before_after_plus1==1 | before_after_plus2==1 | before_after_plus3==1
replace post=0 if before_after_dummy_cminus1==1| before_after_dummy_cminus2==1 | before_after_dummy_cminus3==1 | before_after_dummy_minus1==1 | before_after_dummy_minus2==1 | before_after_dummy_minus3==1

*Please suggest if I did it correctly or if there is any error in the code!