Monte Carlo Simulations using Stata
Oklahoma State University州立大学的Lee C. Adkins教授的课件，
主要内容包括：
Introduction
Software Choices
two methods used for simulation in Stata.
some examples from the paper.
compare speed of simulate, postfile, and gretl.
Paper Examples

pdf文件里面有书签，

为减轻网站流量负担，请各位战友根据论坛币下载。
祝各位战友学习愉快！！！

附件里的code如下：
replace x = theta*L.x + rnormal() in 2
replace u = rho*L.u + rnormal(0,sigma) in 2

replace y = beta*x+delta*L.y + u in 2

reg y x /* b1 */
reg L(0/1).y x /* b2 */
prais L(0/1).y x /* b3 */
reg L(0/2).y L(0/1).x /* b4 */
program regIV, rclass
tempname sim
postfile `sim' gam r2 b biv using results, replace
quietly {
foreach gam of numlist 0.025 0.0375 0.05 0.1 0.15 {
forvalues i = 1/$nmc {
replace u = rnormal()
replace x = `gam'*z+rho*u+rnormal(0,sige)
replace y = slope*x + u
....
}

regIV
use results, clear
by gam, sort: summarize r2 F biv b
by gam, sort: summarize p_ls p_iv
by gam: egen Fbar = mean(F) /* Avg F by gamma */
by gam: egen tslsbar = mean(biv) /* Avg biv by gamma */
by gam: egen olsbar = mean(b) /* Avg b by gamma */
by gam: egen r2bar = mean(r2) /* Avg r2 by gamma */
gen tsls_bias = tslsbar-1 /* IV bias */
gen ols_bias = olsbar-1 /* OLS bias */
gen relb = tsls_bias/ols_bias /* relative bias */
gen rot = 1/(Fbar-1) /* rule of thumb */
gen t = _n /* observation numbers */
keep if mod(t,1000) == 0 /* keep 1 obs per design */
reg relb c.rot##c.rot, noconst /* rel. bias onto rot */
reg relb rot, noconst
test (rot=-1) /* directly proportional? */
}
}
postclose `sim'
end

# Set the sample size and save it in n
nulldata 100
scalar n = $nobs
set seed 3213799
# Set the values of the parameters
scalar slope = 10
scalar sigma = 20
scalar delta = .7
scalar rho = .9
# initialize variables
series u = normal()
series y = normal()
series x = uniform()

loop 400 --progressive --quiet
series e = normal(0,sigma)
series u=rho*u(-1)+e
series y = slope*x + delta*y(-1) + u
ols y const x y(-1)
ols y const x
ar 1; y const x y(-1)
ols y const x y(-1) x(-1) y(-2)
endloop