I conduct Monte Carlo simulations to check whether the observed within school-grade deviations in female proportion are consistent with variation stemming from a random process. For each school-grade, I randomly designate a student as female in each class using a binomial distribution function with p equal to the average proportion of female in the school-grade across all classes.
I then compute the within school-grade standard deviation, using the residuals from regressions of female proportion on school-grade fixed effects. I repeat this process 1,000 times to obtain a 95% empirical confidence interval for the standard deviations in female proportion for each school.
My code is as follows but I am not sure if I am doing the right thing to obtain residual and don’t know how to obtain 95% empirical CI for the standard deviations with program and simulate command.
Any suggestions are deeply appreciated.
Code:
program binomial, rclass drop _all use “simulation_gender” //this is the data where I have schoolID, classID, school_grade(indicator for each school-grade group), p is the average proportion of female in the school-grade across all classes. gen z = rbinomial(100, p) // randomly designate a student as female using a binomial distribution function with p bys classID: egen z_count = total(z==1) bys classID: egen z_classize=total(z~=.) gen z_ratio = z_count/(z_classize-1) if z==0 //z_ratio is the leave-one-out female proportion within class replace z_ratio = (z_count-1)/(z_classize-1) if z==1 replace z_ratio = . if z==. qui areg z_ratio, a(sch_grade) //compute the within school-grade standard deviation predict resid, residuals sum resid return scalar mean = r(mean) return scalar sd = r(sd) end simulate mean=r(mean) sd=r(sd), reps(1000) seed(1234): binomial summarize
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int(classID gradeID schoolID school_grade) float(school_grade p) 1 5 1 1 .501 1 4 1 2 .546 2 4 1 3 .523 2 4 1 3 .523 end
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