Dear all,
I have been reading a lot of time about how to deal with my problem, but I could not find a solution to my problem. I am trying to estimate the next equation for my sample of firms:
exporter is a dummy variable that takes value of 1 when the firm is an exporter, 0 otherwise. corr11 which is my main independent variable is also a dummy variable that takes value of 1 when the firm consider the corruption an obstacle for doing business, 0 otherwise. j2 is a continuous variables that measures the time that the firm spends in bureaucracy stuffs. However, an according to the literature of my field, corr11 is potentially a endogenous regressor, as a consequence I want to estimate my model by using IV approach. As instrument for corr11 I have the variable mean_indreg_corr which is a continuos variable, and for the interaction term corr11*j2 I am using as an instrument mean_indreg_corr*j2. With all this on hand I show you the code that I am using:
However, in other posts I have read that for using ivprobit the endogenous variable must be a continuous one, so, is the code that am I using incorrect? In afirmative case, what method do you recommend me?
Here I show you an extract of my data:
Thank you!
Ibai
I have been reading a lot of time about how to deal with my problem, but I could not find a solution to my problem. I am trying to estimate the next equation for my sample of firms:
Code:
Exporter = b0 + b1*corr11 + b2*j2 + b3*corr11*j2 + controls + u
Code:
gen corr11Xj2 = corr11#c.j2 ivprobit exporter (corr11 corr11Xj2 = c.mean_indreg_corr c.mean_indreg_corr#c.j2) c.j2 c.lnwk14 i.year [pw = wt], vce(cl indus_region) first
Here I show you an extract of my data:
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float(exporter corr11) double j2 float mean_indreg_corrup double wt float lnwk14 double year float indus_region 0 . . .3753685 2.145357847213745 3.178054 2008 6360 0 0 . .3811453 1.3810902833938599 5.043425 2008 6360 0 0 0 .6303345 5.4219794273376465 2.1972246 2008 8055 0 1 . .7982829 3.0827088356018066 4.029806 2008 5040 1 0 . .09067751 1.2318933010101318 4.867535 2008 5550 0 1 0 .577652 1.468322515487671 2.0476928 2008 8055 0 0 . .09520593 5.458652973175049 2.1202636 2008 5550 0 1 0 .57658935 1.6569938659667969 3.295837 2008 8055 0 1 0 .5787058 1.2802809476852417 4.550362 2008 8055 0 1 . .7982829 3.0827088356018066 4.0073333 2008 5040 0 0 . .51142734 24.242000579833984 1.94591 2008 6360 0 0 . .09520593 5.458652973175049 3.10608 2008 5550 1 1 . .57658935 1.6569938659667969 4.248495 2008 8055 0 1 . .7982829 3.0827088356018066 3.970292 2008 5040 0 0 0 .09295917 3.4130375385284424 3.0910425 2008 5550 1 0 0 .09067751 1.2318933010101318 6.052089 2008 5550 0 0 0 .2862001 24.242000579833984 4.518159 2008 12660 0 0 0 .27774113 11 4.5712686 2008 12660 1 1 . .26888573 1.3810902833938599 5.135798 2008 12660 0 1 0 .7836605 13.33569622039795 2.484907 2008 5040 1 0 . .2717792 1.1717313528060913 5.087596 2008 12660 0 . . .3753685 2.145357847213745 3.970292 2008 6360 1 1 . .3603078 2.145357847213745 3.465736 2019 6360 1 0 0 .594837 1.1717313528060913 6.429719 2019 8055 0 1 .1 .2492754 13.33569622039795 1.2039728 2019 12660 1 0 . .09295917 3.4130375385284424 4.060443 2019 5550 0 1 0 .577652 1.468322515487671 2.890372 2019 8055 0 . . .5857432 1.2802809476852417 3.496508 2019 8055 0 0 0 .5971838 1.468322515487671 2.397895 2008 8055 0 1 0 .57776386 1.4483987092971802 3.78419 2008 8055 0 1 0 .26888573 1.3810902833938599 5.628819 2008 12660 0 0 0 .2825752 18.664459228515625 3.4552646 2008 12660 0 0 0 .09520593 5.458652973175049 1.7917595 2008 5550 0 0 . .09067751 1.2318933010101318 4.859812 2013 5550 0 1 0 .5542067 5.4219794273376465 2.995732 2013 8055 1 1 0 .5793117 1.1717313528060913 5.703783 2013 8055 0 0 . .09520593 5.458652973175049 2.782951 2013 5550 1 0 0 .09520593 5.458652973175049 2.8716795 2019 5550 0 0 0 .8779454 13.33569622039795 1.609438 2008 5040 0 1 . .7982829 3.0827088356018066 4.0073333 2008 5040 1 1 0 .5793117 1.1717313528060913 5.521461 2008 8055 0 1 0 .07681484 1.2318933010101318 5.192957 2008 5550 0 0 . .09520593 5.458652973175049 3.555348 2008 5550 0 1 0 .26766273 2.145357847213745 2.730029 2008 12660 0 1 0 .7836605 13.33569622039795 2.3025851 2008 5040 1 0 . .8231907 3.9277756214141846 6.109248 2008 5040 0 0 0 .594837 1.1717313528060913 4.518159 2008 8055 1 0 0 .5986864 1.6569938659667969 4.3609734 2008 8055 0 1 .15 .7982829 3.0827088356018066 3.3322046 2008 5040 0 1 0 .25318915 11 4.323028 2008 12660 0 1 . .7982829 3.0827088356018066 4.0943446 2008 5040 0 1 0 .3603078 2.145357847213745 3.7992275 2008 6360 0 1 0 .57658935 1.6569938659667969 4.1108737 2008 8055 1 1 .2 .36575565 1.3810902833938599 6.336826 2008 6360 0 0 0 .2862001 24.242000579833984 2.6390574 2008 12660 1 0 0 .09520593 5.458652973175049 3.218876 2008 5550 1 0 .05 .09067751 1.2318933010101318 4.744932 2008 5550 0 1 0 .7982829 3.0827088356018066 3.218876 2008 5040 1 0 0 .5971838 1.468322515487671 . 2008 8055 0 0 .1 .6303345 5.4219794273376465 1.7917595 2008 8055 0 1 . .7836605 13.33569622039795 2.484907 2008 5040 1 0 .05 .27190354 1.3810902833938599 4.6051702 2008 12660 0 0 0 .2862001 24.242000579833984 1.609438 2008 12660 0 0 . .2862001 24.242000579833984 2.0583882 2008 12660 0 0 . .28932956 28.944726943969727 . 2008 12660 0 1 0 .23044406 24.242000579833984 2.484907 2008 12660 1 1 . .7982829 3.0827088356018066 3.277145 2008 5040 0 0 0 .2862001 24.242000579833984 2.944439 2008 12660 0 0 0 .09520593 5.458652973175049 2.3025851 2008 5550 0 0 . .09520593 5.458652973175049 2.3671236 2008 5550 1 1 0 .2684447 1.6569938659667969 4.1972017 2008 12660 0 1 0 .577652 1.468322515487671 2.3025851 2008 8055 0 1 0 .23044406 24.242000579833984 1.89712 2008 12660 0 1 0 .5542067 5.4219794273376465 1.6739764 2019 8055 0 0 0 .2825752 18.664459228515625 3.465736 2019 12660 0 0 0 .5986864 1.6569938659667969 2.995732 2019 8055 1 0 0 .09520593 5.458652973175049 2.944439 2008 5550 0 0 0 .2862001 24.242000579833984 2.833213 2008 12660 0 0 0 .09295917 3.4130375385284424 3.912023 2008 5550 0 0 0 .5971838 1.468322515487671 2.70805 2013 8055 0 0 . .3844191 2.145357847213745 2.1400661 2019 6360 0 0 0 .27206755 1.6569938659667969 3.8501475 2019 12660 0 1 .3 .7836605 13.33569622039795 2.6855774 2019 5040 1 0 0 .09295917 3.4130375385284424 3.772761 2019 5550 1 0 0 .27774113 11 4.3820267 2008 12660 0 0 0 .2825752 18.664459228515625 3.135494 2008 12660 0 1 0 .577652 1.468322515487671 . 2008 8055 0 0 0 .2862001 24.242000579833984 1.9694406 2008 12660 1 1 . .24019106 18.664459228515625 3.0445225 2008 12660 1 0 0 .27774113 11 5.192957 2008 12660 0 1 .02 .7836605 13.33569622039795 1.3862944 2008 5040 0 1 . .14896 24.242000579833984 2.70805 2008 6360 1 1 .1 .26766273 2.145357847213745 3.4011974 2008 12660 0 1 . .3603078 2.145357847213745 4.3820267 2008 6360 0 0 0 .6303345 5.4219794273376465 2.1400661 2008 8055 0 1 .07 .7836605 13.33569622039795 2.3025851 2008 5040 0 0 . .3811453 1.3810902833938599 5.147494 2008 6360 0 0 0 .09295917 3.4130375385284424 3.232121 2013 5550 0 1 0 .57776386 1.4483987092971802 3.64632 2019 8055 0 0 0 .51142734 24.242000579833984 2.3025851 2019 6360 end label values j2 J2 label def J2 0 "No time was spent", modify
Thank you!
Ibai
