Assignment

One.do

									*Part I*
*** Setup
	clear all //clear any open datasets
	cls //clear the screen
	version 15
	set more off
	set linesize 255
	
*** Generating a random varibale 
	set obs 100000
	set seed 1343758
	gen t=runiform(0,100)
	replace t = round(t)

*** Summary statistics of theta 
	table t
	codebook t
	sum 
	des

*** Set up equations value 
	gen a = 100
	gen b = 2
	gen c = 150 

*** Set up equations for return on education 
	gen We = a + (b*t)
	gen Wne = t 
	gen Cost_diff = (We - Wne)/c
	gen thres = 1 + .0175

*** Gets an education 
	sum  Cost_diff if Cost_diff >= thres

*** Does not get an education 
	sum  Cost_diff if Cost_diff < thres

Two.do

clear all //clear any open datasets
	
*** Creating a uniform distribution 0 - 100 for N = 10 people
	set obs 10
	set seed 188
	gen n10=runiform(0,100)
	replace n10 = round(n10)
	sum n10
	
*** Store mean 
return list
gen mn10 = r(mean)
sum mn10

forvalues n10 = 1/1000 {
	gen `n10'
	replace `n10' = round(`n10')
	sum `n10'
}
         

Check lines 29-30 in Part I. I don't think this is the right way to model the investment decision. Think about comparing the costs to the benefits using >.

For Part 2, here is a similar loop to get you started:

cap mat drop results

forvalues obs = 1/5 {

	forvalues iter = 1/1000 {
		
		clear
		set obs `=10^`obs''
		gen val = 
		
		sum val
			mat results = nullmat(results) ///
				\ [`r(mean)',`=10^`obs'']
	
	}

}

clear
svmat results

I'm sure there are errors in my code as I'm doing this on my phone and you will need to read documentation with functions you're not familiar with but this should be a basic framework for almost all simulations.