Dear members
I am trying to assess the effect of staggered country-level policy mandate across countries on firms' annual report issuance, which is a dummy variable = 1 if a firm issues a report, and zero otherwise.
I have estimated the following regression model :
. reghdfe report_issuance treatedxmandate log_assets profit_assets leverage Tangability log_followers ,absorb ( indyear firm_id ) cluster ( firm_id )
Both my DV and IV are dummy variables. Running reghdfe may not be appropriate given that my dv is a dummy variable so I have used logit and clogit and they either do not work or the Stata output goes for many hours without showing me a result. What is the appropriate model to use and the Stata command for a binary dependent variable given the above data?
Thank you for your help in advance
here is an example of my data:
input float(report_issuance treatedxmandate log_assets profit_assets leverage Tangability log_followers indyear firm_id)
0 1 22.09539 .10896342 .26697525 .3888849 3.0445225 1 1598
0 0 21.231033 .1333768 .25623 .3651632 1.3862944 1 1276
0 1 19.60781 .06340366 .28342476 .7408151 0 2 651
0 1 21.50294 .12386908 .24245936 .415777 1.3862944 2 1276
0 1 22.384005 .12970378 .34663895 .316556 2.944439 2 1598
0 0 20.729723 .1231608 .2824437 .7142625 1.609438 3 1946
1 1 22.234505 .06173645 .3486554 .3412117 3.135494 3 1598
0 1 19.721035 .08277166 .2656149 .7531047 0 3 651
0 0 21.722145 .21519312 .0844603 .4052416 1.609438 3 1276
0 0 21.88899 .1665505 .000666412 .4719283 1.0986123 4 1276
0 0 20.93306 .1170604 .24783103 .7164966 1.3862944 4 1946
0 1 19.89518 .08028212 .27235544 .7035513 .6931472 4 651
1 1 22.398113 .11755613 .27583528 .3186312 3.218876 4 1598
0 1 20.040924 .08674576 .22891605 .6852555 0 5 651
0 0 21.12707 .12655964 .2334737 .6884319 1.94591 5 1946
1 1 22.59383 .1133817 .27531928 .332152 3.2580965 5 1598
0 1 21.37992 .11948904 .27908602 .6042764 2.484907 6 1946
1 1 22.70445 .0778197 .3532118 .32933185 3.295837 6 1598
0 1 20.1487 .03274242 .2905904 .7305714 .6931472 6 651
0 1 20.198055 .02277854 .3387834 .7358052 0 7 651
0 1 20.22527 .1626201 .05762431 .7884482 0 7 631
0 0 21.76731 -.1819071 0 .51496255 0 7 1276
1 1 22.744257 .08262318 .3071707 .2220144 3.295837 7 1598
0 1 21.292013 .11623523 .27730125 .6638483 2.70805 7 1946
0 1 21.40697 .08378931 .2943257 .6559136 2.833213 8 1946
0 1 21.64648 -.12554957 .0018827615 .5164677 0 8 1276
0 1 20.2669 .04594868 .05514361 .7556307 0 8 631
0 1 20.21207 .03517542 .32492355 .7691963 1.0986123 8 651
1 1 22.28374 .2200513 .4921244 .4192886 3.0910425 8 1598
0 1 21.365404 .055399 .2604782 .681021 2.70805 9 1946
1 1 21.98892 .01255649 .4966125 .4967171 3.0445225 9 1598
0 1 21.083815 -.30459535 .0004688987 .4241729 0 9 1276
I am trying to assess the effect of staggered country-level policy mandate across countries on firms' annual report issuance, which is a dummy variable = 1 if a firm issues a report, and zero otherwise.
I have estimated the following regression model :
. reghdfe report_issuance treatedxmandate log_assets profit_assets leverage Tangability log_followers ,absorb ( indyear firm_id ) cluster ( firm_id )
Both my DV and IV are dummy variables. Running reghdfe may not be appropriate given that my dv is a dummy variable so I have used logit and clogit and they either do not work or the Stata output goes for many hours without showing me a result. What is the appropriate model to use and the Stata command for a binary dependent variable given the above data?
Thank you for your help in advance
here is an example of my data:
input float(report_issuance treatedxmandate log_assets profit_assets leverage Tangability log_followers indyear firm_id)
0 1 22.09539 .10896342 .26697525 .3888849 3.0445225 1 1598
0 0 21.231033 .1333768 .25623 .3651632 1.3862944 1 1276
0 1 19.60781 .06340366 .28342476 .7408151 0 2 651
0 1 21.50294 .12386908 .24245936 .415777 1.3862944 2 1276
0 1 22.384005 .12970378 .34663895 .316556 2.944439 2 1598
0 0 20.729723 .1231608 .2824437 .7142625 1.609438 3 1946
1 1 22.234505 .06173645 .3486554 .3412117 3.135494 3 1598
0 1 19.721035 .08277166 .2656149 .7531047 0 3 651
0 0 21.722145 .21519312 .0844603 .4052416 1.609438 3 1276
0 0 21.88899 .1665505 .000666412 .4719283 1.0986123 4 1276
0 0 20.93306 .1170604 .24783103 .7164966 1.3862944 4 1946
0 1 19.89518 .08028212 .27235544 .7035513 .6931472 4 651
1 1 22.398113 .11755613 .27583528 .3186312 3.218876 4 1598
0 1 20.040924 .08674576 .22891605 .6852555 0 5 651
0 0 21.12707 .12655964 .2334737 .6884319 1.94591 5 1946
1 1 22.59383 .1133817 .27531928 .332152 3.2580965 5 1598
0 1 21.37992 .11948904 .27908602 .6042764 2.484907 6 1946
1 1 22.70445 .0778197 .3532118 .32933185 3.295837 6 1598
0 1 20.1487 .03274242 .2905904 .7305714 .6931472 6 651
0 1 20.198055 .02277854 .3387834 .7358052 0 7 651
0 1 20.22527 .1626201 .05762431 .7884482 0 7 631
0 0 21.76731 -.1819071 0 .51496255 0 7 1276
1 1 22.744257 .08262318 .3071707 .2220144 3.295837 7 1598
0 1 21.292013 .11623523 .27730125 .6638483 2.70805 7 1946
0 1 21.40697 .08378931 .2943257 .6559136 2.833213 8 1946
0 1 21.64648 -.12554957 .0018827615 .5164677 0 8 1276
0 1 20.2669 .04594868 .05514361 .7556307 0 8 631
0 1 20.21207 .03517542 .32492355 .7691963 1.0986123 8 651
1 1 22.28374 .2200513 .4921244 .4192886 3.0910425 8 1598
0 1 21.365404 .055399 .2604782 .681021 2.70805 9 1946
1 1 21.98892 .01255649 .4966125 .4967171 3.0445225 9 1598
0 1 21.083815 -.30459535 .0004688987 .4241729 0 9 1276
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