* bs0.do	1/06
* bootstrap method
* location and shape shift quantities
* 3 quantiles

set matsize 800

* k= # of covariates + cons
local k=14
local k1=`k'-1

* initial
forvalues j=0/2 {
use e`j'1, clear
qui mstore e
mat ren e e`j'
}

forvalues j=0/2 {
forvalues i=2/500 {
use e`j'`i', clear
qui mstore e
mat e`j'=e`j'\e
mat drop e
}
}

forvalues j=0/2 {
qui svmat e`j'
}

* mean of estimate (point estimate)
* sd of estimates (se)
* percentile-method (95% ci)
forvalues j=0/2 {
forvalues i=1/`k' {
pctile x=e`j'`i', nq(40)
sort x
qui gen x0=x if _n==20
qui gen x1=x if _n==1
qui gen x2=x if _n==39
egen em`j'`i'=max(x0)
egen el`j'`i'=max(x1)
egen eu`j'`i'=max(x2)
drop x x0 x1 x2
sum e`j'`i' em`j'`i' el`j'`i' eu`j'`i'
}
}

* SCS scale shift 
forvalues i=1/`k1' {
gen sc1s`i'=e2`i'-e0`i'
pctile x=sc1s`i', nq(40)
sort x
qui gen x0=x if _n==20
qui gen x1=x if _n==1
qui gen x2=x if _n==39
egen sc1sm`i'=max(x0)
egen sc1sl`i'=max(x1)
egen sc1su`i'=max(x2)
drop x x0 x1 x2
sum sc1s`i' sc1sm`i' sc1sl`i' sc1su`i'
}

* SKS skewedness shift
* SKS e2(.975) - e1(.5) and e1(.5) - e0(.025)
* i for covariate, k for constant
forvalues i=1/`k1' {
gen nu=(e2`i'+e2`k'-e1`i'-e1`k')/(e2`k'-e1`k')
gen de=(e1`i'+e1`k'-e0`i'-e0`k')/(e1`k'-e0`k')
gen sk1s`i'=nu/de
drop nu de
pctile x=sk1s`i', nq(40)
sort x
qui gen x0=x if _n==20
qui gen x1=x if _n==1
qui gen x2=x if _n==39
egen sk1sm`i'=max(x0)
egen sk1sl`i'=max(x1)
egen sk1su`i'=max(x2)
drop x x0 x1 x2
sum sk1s`i' sk1sm`i' sk1sl`i' sk1su`i'
}